Black pepper can be planted in tandem with shade trees

Kerala is the second largest coffee producing region in the country, responsible for 23% of India’s total, and the district of Wayanad produces 90% of all the coffee grown in Kerala.Wayanad is located in the Western Ghats mountain range, along the southwest coast of the Indian subcontinent, and receives a majority of its precipitation from the southwest monsoon period.Thus it is an effective region in which to examine the potential effects of climate change, and alterations in the South Indian monsoon, on coffee production in India. In the following analysis, current trends in the local climatic conditions of Wayanad are examined and discussed as they pertain to coffee production. The roles of global warming and large-scale climate variability modes such as El Niño Southern Oscillation and Indian Ocean Dipole as influential factors in Wayanad’s local trends are investigated. Data suggests strong weather trends during the month of June, which may be significant for the production cycle of the coffee plant. Other factors examined include early Spring precipitation, which triggers the blossoming of the coffee flower, and November precipitation, which provides the moisture that sustains coffee plants during the immediately proceeding dry season. As seen above, daily maximum temperatures were found to be increasing over time in the months of June, July, and August. Daily maximum temperature was found to be decreasing over time by a negligible amount in September. This temperature increase could be attributed to the global-scale warming trend, but could also be due to other local factors, and further study would be necessary to determine this conclusively. Monthly precipitation values were found to be decreasing significantly in June,tower garden and no significant precipitation trends were found in other monsoon months. This implies a delayed onset of the monsoon season, which is consistent with other findings. Another metric for monsoon onset, which is the first day of June with a recorded precipitation of over 25mm, also aligns with this finding.

Total precipitation for the entirety of the monsoon period also displays a decreasing trend, suggesting an overall weakening of the monsoon that is also consistent with other findings for the region.Thus it can be concluded that the local climate of Wayanad is experiencing changes in the monsoon that are consistent with the broader region. While canopy trees can reduce annual yields of coffee and other crops, integrated multi-species systems are very likely to be more resilient to climate change in the near future. According to Prasanth one of the farmers interviewed during this study, while a full sun monoculture of robusta coffee in Kerala will yield approximately 900 kg / acre, a shade-grown system will yield only 700 kg / acre. However, inter cropping with canopy trees provides cooler air temperatures, which is likely to protect under story crops such as coffee from future heat waves. Shade trees have been shown to decrease leaf temperature of coffee plants by up to 4˚C in sub-optimally hot conditions . In addition, shade creates cooler soil temperatures, which increases microbial and mycorrhizal activity. This in turn increases uptake of nutrients in crop plants, which promotes better plant health and can increase yields . A robust canopy layer has also been shown to decrease both soil evaporation and crop transpiration, which can substantially increase soil moisture available for crops to use during photosynthesis . Canopy layers also reduce precipitation velocity, which reduces erosion and crop damage . If adopted at a large enough scale, reinstating a living canopy layer across Kerala is likely to be the state’s single most effective strategy for agricultural resilience in the face of climate change. Shade trees can also provide supplemental income to buffer or even surpass coffee yield losses, if commodity-producing trees are used. Shade trees well suited for coffee agroforestry in Kerala include rubber,mango , areca nut , teak , silver oak , and jackfruit . It has been recently suggested that mango trees are particularly well suited for growth alongside coffee due to their tendency to draw water from the deeper subsoil level, thereby reducing competition for water, as coffee draws the majority of its water from the topsoil layer .

Studies indicate that optimal shade levels for coffee production are approximately 20-40%, however this can vary based on local ecological and economic conditions . If species are selected carefully based on local conditions, shade trees can provide substantial economic synergies to coffee agroforestry systems. In conjunction with a canopy layer, additional crop species can be utilized to increase farm productivity-per-acre.The pepper plant, which is a vine, can be made to grow using the shade trees for support. Pepper also has a different harvest season than coffee, which is a useful synergy from a logistic perspective. An under story of turmeric, ginger, and other spices can be planted as well, or alternatively an under story of nitrogen-fixing cover crops to increase soil fertility.Building soil health, which for these purposes can be translated to increasing soil carbon content, water holding capacity, and microbial richness, can significantly buffer a farmer’s vulnerability to economic and environmental uncertainties. An increasing number of studies suggest that the single best metric for long-term soil fertility is carbon content. While fertilization with synthetic nitrogen, phosphorous and potassium compounds can lead to short-term yield boosts, building organic content in soil vastly improves long-term yields by increasing water retention, reducing erosion, and building healthy microbial ecosystems. . A review paper encompassing a multitude of crop species in agricultural systems across the globe concluded that soils with higher carbon content consistently produced higher yields, especially in dry years . Soil carbon content can be increased by mulching, composting, or cover cropping. All of these practices require little to no technology and can be implemented at very low cost to the farmer. Organic matter can be added to soils in the form of manure, crop residues, or waste pulp from coffee processing. In Kerala, these products are often dried and burned for use as cooking fuel. However, some products, most notably coffee pulp, often goes to waste. Art Donnelly, founder of the Estufa Finca project, has invented a low-cost cooking stove that uses a dualchambered, oxygen-restricted combustion system to burn organic coffee waste products in a way that produces both a clean-burning cooking flame and carbonrich charcoal, or biochar. This charcoal can be applied to fields directly, and a number of studies find this application to boost both microbial activity and crop yields . The biochar can also be fermented to produce a microberich fertilizer. This process is also low-cost and low-tech. A 2017 account of a Costa Rican farmer named Echeverría describes the process: “This low-tech microbial inoculant employs a sourdough-like starter to brew up a bio-fertilizer rich in mycorrhyzal fungi.

To get it going, Echeverría collects a bag full of organic matter from the forest floor, making sure that it contains white fungal hyphae. Next, she removes all the big leaves and mixes it up with rice bran and molasses. She then covers it with a plastic bag in 60-liter, screw-top containers and lets it ferment for a month and a half.” . An account of Gabriel Umaña, an agent for the Costa Rica Ministry of Agriculture,stacking flower pot tower attests that application of fermented biochar even “synchronizes maturity among coffee bushes. This helps farmers with a major logistical problem, as coffee beans must be sold when ripe.” . Notably, biochar relies on the synthesis of carbon-based plant matter via photosynthesis, and the process of incomplete combustion returns a portion of this carbon to the soil. As such, it is the only known form of energy production that is net carbon negative. In addition to building soil carbon content, employing no-till farming practices, robust mulching or cover cropping, and diverse crop rotations are essential to promoting soil health. Frequent tillage has been shown to result in decreased soil carbon and loss of fertility . Covering the soil surface with mulch, cover crops, or other organic residues has been proven to conserve soil moisture and reduce erosion . Surface mulch from crop residues has been shown to affect crop yields due to its variety of physical, chemical, and biological impacts on soil characteristics . Long-term studies have shown that agricultural practices that combine crop rotations, consistent surface residue, and reduced tillage result in soils with higher levels of carbon and nitrogen . Crop rotation is also associated with increased microbial diversity in soils, which in turn reduces risk of pest outbreaks due to natural biological control mechanisms . Through MSSRF, the State of Kerala’s Ministry of Agriculture already provides stipends to farmers to keep native species of shade trees on their properties. In addition, a separate Tree Stipend Program has recently been developed that engages the private sector. In this program, the government sponsors banks to give financial support to farmers who plant trees. In exchange for the initial investment to plant the trees, the banks then own the assets associated with the value of the tree on the farmer’s property, much like a lien. However, these programs should be provided much more funding. According to Prasanth, a typical stipend to keep a rosewood tree is approximately 700 rupees per year , whereas the profits from cutting the tree and harvesting its timber is closer to 70,000 rupees . As such, the tree stipends should be increased by at least two orders of magnitude in order to be a policy that is effective at retaining participants. In lieu of providing frivolous crop insurance payouts that subsidize bad farming practices, it is recommended to incentivize practices that build topsoil and increase soil carbon content.

During the course of this study, soil carbon content was found to be the single most applicable metric correlated with sustainable and resilient farming practices. Establishing simple incentives based on a single metric reduces organizational costs for the governing body, while simultaneously encouraging a diverse array of carbon-sequestering farming practices that work synergistically to build economic and climate resilient agriculture. Measuring soil carbon content is low-effort and low-cost, and the monitoring efforts that would be required to award financial incentives responsibly are likely to be fairly cost-effective relative to other similar incentive efforts. This policy could be achieved through subsidies, stipends, or other incentive measures. In order for any of these policies to be effective, awareness and education must be brought directly to the farmers. Studies indicate that one of the most effective methods to encourage the adoption of new farming practices is the establishment of full-scale demonstration farms within the agricultural communities . MSSRF’s Botanical Garden presents a fortuitous opportunity in Kerala, as it is already established, staffed, and well known within the community. This would be an excellent location to establish a pilot program for carbon farming workshops, in which information and hands-on lessons about regenerating topsoil and soil carbon can be provided. If successful, these demonstration farms could be replicated in other communities. In an effort to increase consumer awareness of the current state of the coffee commodity market and its effects on smallholder producers, an amended version of the Introduction section of this report is being published and distributed among coffee shops in the San Diego area, along with a link to the full report. In addition to the article, a list of San Diego coffee shops that implement ethical coffee purchasing practices is included, to promote awareness of socially responsible coffee companies. The publication encourages consumers to support importers with business models that don’t undermine the livelihoods of producers and the ecosystems that sustain them. Engaging consumers in a way that changes their preferences is one of the most effective ways to enact change in market economies.In addition to consumer outreach for the sake of increasing awareness, a fundraising effort was established to support the M. S. Swaminathan Research Fund and the work they are doing to improve conditions for farmers in Kerala. During the month of July 2019, a portion of the proceeds from merchandise sold at Bird Rock Coffee Roasters will go to MSSRF. This partnership represents an easily replicable business model for any coffee business that wants to connect consumers with producers in a way that builds resilience and ethical practices into their supply chain.

Improving agricultural production and profits is an important component of poverty alleviation

Randomized evaluations of the agronomic productivity gains from new crops or agricultural techniques have been common in the agricultural field for many years. More recent is an approach to agriculture that aims to conduct ‘effectiveness’ trials, incorporating real-world issues of access and adoption among smallholder farmers, rather than the idealized ‘efficacy’ trials produced using experimental test plots. Tackling the impacts of agricultural interventions outside of the test plot introduces issues at the heart of economics, such as transaction costs, social interactions, marketing, finance, and contracting as we think carefully about the decision to adopt. Thinking of the smallholder farm as a small business, this decision should be driven by profitability. The core contribution of RCTs is their ability to clearly trace causality between the constraints to agricultural technology adoption, adoption itself and final outcomes . Randomized experimental evaluations allow researchers to isolate the causal impact of a program from other confounding factors—such as price, weather, or access to credit—which are simultaneously changing over time and across regions 2 . Carefully designed experiments allow us to identify whether specific constraints to adoption are binding, and measure the impacts of a technology when adopted in farmers’ actual fields. These evaluations speak to the effectiveness of specific approaches to achieving agricultural technology adoption for improved smallholder productivity and welfare.The Agricultural Technology Adoption Initiative was founded in 2009 to increase the quantity and quality of experimental evidence in developing-country agriculture. ATAI aims to serve as a mechanism to generate, aggregate,livestock fodder system and summarize research for policy outreach on the adoption of agricultural innovations by smallholders in Sub-Saharan Africa and South Asia.

ATAI exclusively funds randomized controlled trials, and pilot work that lays the groundwork for future RCTs, and was organized intellectually around understanding how a set of specific constraints held back technology adoption. Because of this methodological focus, the resulting evidence is primarily on interventions targeted at the individual or household level, although we also report on studies in areas such as input and output markets that attempt to drive outcomes at more aggregated levels. Even within this domain, we have a distribution of studies that is purposive, driven by the questions asked by our affiliated investigators, and by the technical feasibility of running randomized trials. We use the structure of the ATAI constraints to adoption to help summarize the experimental evidence, aggregating individual, internally valid studies around these common themes. This produces an evidence base that is far from comprehensive in terms of the important issues in agricultural development, but is broader than would have been produced by a more tightly structured replication-focused research initiative and does provide a relatively clear guide to what makes specific interventions attractive in terms of evidence-based funding. Throughout the world, 63% of those living under $1.25 per day are working in agriculture .Ligon and Sadoulet show the importance of economic growth in the agriculture sector for the livelihoods of the poorest households: a one percent growth in GDP that originates from agriculture correlates with a 5.6 percentage point increase in expenditures among the poorest decile of the population, a 4.45 percentage point increase for the bottom 30%, while “growth from non-agriculture sectors does not appear to have a significant effect on expenditure growth for the poorest 50%.” The Green Revolution of the 1960s saw the spread of agricultural technologies to less industrialized nations, and large agricultural productivity gains particularly in East Asia.

Yet technological innovations have not similarly spread to transform agricultural productivity in Sub-Saharan Africa and parts of South Asia as evident in the lagging adoption of modern varieties and a persistent yield gap between regions. Many African countries have rising private sectors developing agricultural technologies, and research and implementation groups including the CGIAR centers and AGRA continue to develop improved inputs and interventions designed to improve the resilience, profits, and nutrition of African smallholders in particular. Yet these innovations do not appear to have translated into meaningful improvements in yields at the macro-level. FAOSTAT data shows a large gap between low per hectare cereal yields in Africa and South Asia which are on average roughly one third of the per hectare yields in East Asia and OECD countries. Sub-Saharan Africa is particularly lagging behind. In South Asia, land use for cereal production has increased 20% while yields have tripled. In Sub-Saharan Africa, land use for cereal production has more than doubled, while yields have increased by just 80% . The macro picture of fertilizer use over time similarly looks unchanged, with low and stagnant use of fertilizers in mainly rainfed areas like SubSaharan Africa. Fertilizer consumption remains extremely low in SubSaharan Africa compared to other regions. Roughly 16 kilos of fertilizer are used per hectare in SubSaharan Africa, and among all developing countries the average is 26.75 kg/hectare. This figure is much higher in other regions: 344 kg/hectare in East Asia/Pacific, and 159 kg/hectare in South Asia.This clearly demonstrates that the status quo of agricultural production, particularly in Sub-Saharan Africa, remains far below the technological frontier, suggesting missed potential in terms of yields, income, and welfare improvements to food security and nutrition. The specific reasons behind lagging adoption of productivity enhancing technological innovations and persistent yield gaps in rainfed Sub-Saharan Africa and South Asia relative to the rest of the world have been a puzzle in need of policy solutions. Field experiments help us move beyond test plots to explain the continuing puzzle of low technology adoption by smallholder farmers in rainfed areas where agriculture is performing well below the technological frontier. Focusing at the micro-economic level of this challenge, we focus on technology adoption as an outcome that inherently requires smallholder farmers to change their practices.

Behavior changes can include, for example, the adoption of resilient and high-yielding crop varieties or a shift to high-value crops, the purchase and application of complementary inputs such as fertilizers, and the adjustment of farm labor allocated toward specific agronomic practices. Many smallholder farmers face barriers to adopting effective agricultural technologies. These constraints to adoption may be driven by standard economic factors , or may be behavioral . Standard economic explanations consider smallholder farmers as economic agents, building from the conception that “in a well-functioning economy where markets perfectly capture all costs and benefits, and individuals are fully informed and unconstrained, farmers will adopt a technology if they make a profit from adopting it” . This is an important distinction from a world where farmers focus their efforts to maximize their productivity, for example, their crop yields, given increased yields do not necessarily lead to improved welfare. Profitability can be limited by input costs, credit constraints, and market access. Information and labor constraints are also relevant — how well do farmers understand the properties of new technologies, in the absence of opportunities to experiment? What are the additional labor requirements for the use of these new technologies, and how do farmers value their time in input decisions? Jack reviews in detail other dimensions that mediate whether certain technologies “meet the expected profitability condition” for specific farmers. This varies temporally and spatially . This also varies between and within households,hydroponic nft gully particularly when complementary asset or capital investments are needed, or new technologies challenge individual tastes and preferences. Even where markets are functioning well, accessible and profitable technologies may not be adopted for behavioral reasons, such as risk or uncertainty aversion or procrastination, which challenge decision-making even in the best of circumstances. Smallholders’ decision-making is highly complex and conducted in risky and low resource environments. Farmers make interconnected choices over long time frames that are characterized by risks and uncertainty. One of many choices is among a range of potential inputs to production , in contexts with highly variable land, wide ranging and seasonal climatic variation that is growing increasingly extreme given climate change, and unpredictable shocks to their livelihood. New technologies may change the risk or payoff profiles of farming in ways that require us to incorporate other social science insights, for example expected utility theory and behavioral economics, in order to understand perceived benefits at the farmer level. Motivated by addressing the constraints hindering the adoption of new agricultural technologies, ATAI has worked to fund and structure the experimental evidence base across seven primary market inefficiencies that constrain adoption. These are credit5 , risk, information, input and output markets, labor and land market inefficiencies, as well as externalities . These may operate through supply or demand channels, for example by limiting the availability of technologies, information, or financing, and/or dampening demand by lowering expected profits. Lessons from psychology and behavioral economics are considered where they are particularly relevant. Jack motivates the focus on constraints to adoption, rather than specific technologies, as a framework that helps identify effective strategies to address common inefficiencies and constraints in order to encourage the adoption and use of more than one technology. ATAI uses this conceptual framework of seven constraints to drive its research competitions.

Randomized evaluations are selected for ATAI funding based not only on methodological rigor, logistical viability, and innovation, but also on their potential for both a significant contribution to public knowledge, and practical influence and scalability in related contexts. Field experiments require, by their very nature, durable partnerships with real-world implementation groups that are working directly with smallholder farmers in order to randomize interventions and deliver credible results. Partner organizations may work as agro-dealers, contract farming groups, extension agents, financial service providers, technology developers, or otherwise. ATAI views more favorably studies that evaluate questions of key importance to large-scale program and policy partners, particularly those that are difficult to address without causal evidence, and those that have received less research attention to date. To meet these criteria, technologies under investigation are those where there is credible field data signaling that adoption would prove neither distasteful nor ineffective in target farmers’ contexts, and that the take-up and use of a technology is likely to prove utility-enhancing, profitable, and welfare-increasing for smallholder farmers and others along agricultural value chains. For such promising under-adopted technologies, ATAI funds social science field experiments to provide evidence on the strategies that work in helping farmers adopt, and ultimately benefit from, these technologies. In the sections that follow, we summarize particular components of the evidence base given the accumulation of ATAI-generated experimental evidence in four areas: credit and savings, risk, information, and input and output market inefficiencies. This does not imply that the latter three constraints to adoption, i.e. externalities and land and labor markets, are excluded from this chapter because they do not bind or do not deserve further investigation. These topics are not covered here simply because there is less rigorous micro-evidence given the difficulty of examining them through the lens of RCTs6 . This is not intended to be an exhaustive review. ATAI-funded studies are often presented in greater detail given our familiarity with their contributions. Each section begins by motivating the specific constraint to Agricultural income streams are characterized by large cash inflows once or twice a year that do not align well with specific times when farmers need access to capital to either make agricultural investments or, for example, pay school fees. If there is limited access to credit in an area, farmers may not have cash on hand to make agricultural productivity investments unless they are able to save, or can afford the potentially high interest rates of informal lending. However, saving can be difficult for farmers given their limited resources, a variety of demands on their money, and the seasonal cycle of production and prices of their agricultural production. Credit and saving products could help farmers make investments in inputs and other technologies by making cash available when needed. Yet many developing countries, and particularly rural areas, have limited access to formal financial services that could provide this liquidity. Credit constraints have been reflected in farmers self-reports , and are associated with less use of productive inputs like high-yielding varieties . On the supply side, formal financial service providers are often unwilling or unable to serve smallholders.

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.

We turn now to the estimation of the hedonic farmland value equation

This is because, although non-federal surface water is generally not subsidized, it is priced on the basis of historic cost, which is generally far below the current replacement cost of this capital. In summary, the economic effects of climate change on agriculture need to be assessed differently for counties on either side of the 100th meridian, using different variables and different regression equations. Because of data constraints, our analysis here focuses on the effect of climate on farmland values in counties east of the 100th meridian. Our sample comprises approximately 80 percent of the counties and 72 percent of all farmland value in the United States.The dependent variable in our hedonic model is the county average value of land and buildings per acre as reported in the 1982, 1987, 1992, and 1997 Censuses of Agriculture. We have translated all numbers into 1997 dollars using the GDP implicit price deflator to make them comparable. It has been customary in the hedonic literature to use as explanatory variables soil and climatic variables evaluated at the centroid of a county. However, soils and climatic conditions can vary significantly within a county and the estimated value at the centroid might be quite different from what farmers experience. To more accurately reflect this reality, we therefore average the soil characteristics over all the farmland area in a county.The agricultural area is used as a cookie-cutter for our exogenous variables, i.e., we average the climate and soil variables over all farmland areas in a county. All soil variables are taken from STATSGO, a country-wide soil database that aggregates similar soils to polygons.The climatic variables are derived from the PRISM climate grid,10 litre plant pots a small-scale climate history developed by the Spatial Climate Analysis Service at Oregon State University and widely used by professional weather services. It provides the daily minimum and maximum temperature and precipitation averaged over a monthly time-scale for a 2.5 mile x 2.5 mile grid in the coterminous United States, i.e., more than 800,000 grid cells, for the years 1895 to 2003.

The 2.5 mile x 2.5 mile climate polygons are intersected with the agricultural area to derive the agricultural area in each polygon. The climatic variables in a county are simply the area-weighted average of the variables for each climate grid. In this analysis we use the monthly average temperature and precipitation for the 30 years preceding each census year.The existing literature has generally represented the effect of climate on agriculture by using the monthly averages for January, April, July and October.However, from an agronomic perspective, this approach is less than optimal. First, except for winter wheat, most field crops are not in the ground in January; most are planted in April or May and harvested in September or October . Second, plant growth depends on exposure to moisture and heat throughout the growing season, albeit in different ways at different periods in the plant’s life cycle; therefore, including weather variables for April and July, but not May, June, August or September, can produce a distorted representation of how crops respond to ambient weather conditions. The agronomic literature typically represents the effects of temperature on plant growth in terms of cumulative exposure to heat, while recognizing that plant growth is partly nonlinear in temperature. Agronomists postulate that plant growth is linear in temperature only within a certain range, between specific lower and upper thresholds; there is a plateau at the upper threshold beyond which higher temperatures become harmful. This agronomic relationship is captured through the concept of degree days, defined as the sum of degrees above a lower baseline and below an upper threshold during the growing season. Here we follow the formulation of Ritchie and NeSmith and set the lower threshold equal to 8◦C and the upper threshold to 32◦C. In other words, a day with a temperature below 8◦C results inzero degree days; a day with a temperature between 8◦C and 32◦C contributes the number of degrees above 8◦C; and a day with a temperature above 32◦C degrees contributes 24 degree days. Degree days are then summed over the growing period, represented here by the months from April through September.Following Ritchie and NeSmith , the level beyond which temperature increases become harmful is set at 34◦C.

A complication with degree days is that the concept is based on daily temperature while our climate records consist of monthly temperature averages. Thom develops the necessary relationship between daily and monthly temperature variables under the assumption of normality. This relationship is used to infer the standard deviation of daily temperature variables from monthly records. Degree days are then derived using the inverse Mills ratio to account for the truncation of the temperature variable.Before we present our regression results we first examine whether the spatial correlation of the error terms as described in the previous section is indeed present. We conduct three tests of spatial correlation for all counties east of the 100th meridian using the same set of exogenous variables as in the estimation of the hedonic equation in Table 3 below, including state fixed effects. One test is the Moran-I statistic . However, since this does not have a clear alternative hypothesis, we supplement it with two Lagrange-Multiplier tests involving an alternative of spatial dependence, the LM-ERR test and LM-EL test. The results are shown in the first three rows of Table 2. Note that they are rather insensitive to the chosen weighting matrix.The spatial correlation of the error terms is quite large and omitting it will seriously overstate the true t-values. For example, the t-values using standard OLS that does not correct for the spatial correlation or the heteroscedasticity of the error terms are up to nine times as large, with an average value of 2.2 for the model presented in the first column of Table 3. In the following we use a two stage procedure. In the first stage we estimate the parameter of spatial correlation and premultiply the data by . In the second stage we estimate the model and use White’s heteroscedasticity consistent estimator to account for the heteroscedasticity of the error terms. In previous climate assessments, it has been customary to estimate a linear regression model. Since farmland values have to remain non-negative, and given the highly skewed distribution of farmland values in Table 1 a semi-log model appears preferable. To determine which model better fits the data, we conduct a PE-test . We use 10,000 bootstrap simulations to get a better approximation of the finite sample distribution of the estimate. The t-value for rejecting the linear model in favor of the semilog model is 873, while the t-value of rejecting the semi-log model in favor of the linear model is 0.01.

We therefore focus the remainder of our analysis on the semi-log model.Results of the log-linear hedonic regression under the Queen standardized weighting matrix are displayed in the first two columns of Table 3. We present results with and without state fixed effects. The reason for including fixed effects is that this can control for the possibility that there are unobserved characteristics common to all farms within a state,40 litre plant pots such as state-specific taxes and uneven incidence of crop subsidies due to differences in cropping patterns across states. The concern is that the identification of the climate coefficients in the hedonic model might otherwise come primarily from variation in government programs that target specific crops. However, it should be noted that since we rely on a nonlinear functional form, the estimation procedure still uses variation between states in the identification of the coefficients. We find that inclusion of fixed effects does not reduce the significance level of the climatic variables. At the same time, the parameter of spatial correlation is virtually unchanged when we include fixed effects, suggesting that there are indeed spill-over effects that are based on spatial proximity rather than an administrative assignment to a particular state. The estimated coefficients on the climatic variables are consistent with the agronomic literature. The optimal number of growing degree days in the 8◦C − 32◦C range peaks at 2400 degree days for the pooled model in column 1 of Table 3. This is close to the optimal growing condition for many agricultural commodities when one adjusts for the length of the growing season . Degree days above 34◦C are always harmful.13 Precipitation peaks at 79 cm or approximately 31 inches, which also is close to the water requirements of many crops, when adjusted for the length of the growing season. Other variables have intuitive signs as well. Income per capita and population density are important and highly significant determinants of farmland value: higher population pressure translates into higher farmland values, albeit at a decreasing rate. Similarly, higher incomes drive up the price of farmland. Two soil variables are significant at the 5% level in the pooled model: better soils, as measured by a soil quality index, result in higher farmland values; and a lower minimum permeability, which indicates drainage problems, reduces farmland value. The effect of the former is quite large: farmland with 100% of soils in the best soil class categories are 35% more valuable compared to farmland with 0% in the top soil classes. The variable K-factor is significant at the 10% level in three out of the five regressions using state fixed effects. It indicates higher erodibility of the fertile top soil, which is harmful. Percent clay frequently switches sign and is not significant in most models; neither is the average water capacity of the soil. We have suggested that degree days and precipitation over the growing season better represent the effect of climate on agriculture than the alternative specification of monthly averages of untransformed temperature and precipitation.

To assess this claim, we conduct an encompassing test to determine which model is better at predicting the effects of climate change. In order to do so, we split the sample into two subsets: the northern-most 85% of the counties in our sample are used to estimate the parameters of both models in order to derive the prediction error for the southern-most 15%, i.e., we see which model calibrated on moderate temperatures is better at predicting the values for warmer temperatures. The results offer clear confirmation of the superiority of the degree days model. Even though this model has less than one third the number of climate variables included in the alternative, we can reject the null hypothesis of equal forecasting accuracy in favor of the degree days model with a t-statistic of 2.94 .14 Kaufmann emphasizes that the parameter estimates in the model using undemeaned climate variables often vary between models. This is not surprising in light of the strong multi-colinearity between the climate variables that leads to frequent switching of the parameter estimates, sometimes with large marginal effects. This can be seen in our data as well, as shown in an appendix available from the authors on request. Summarizing briefly, when the monthly climatic variables for January, April, July and October are included, the only variables which are significant in the pooled model between all census years are July temperature and April and October precipitation ; none of the other 10 monthly climate variables in the pooled model is significant even at the 10% level. Further, the coefficients on July temperature imply that farmland value peaks at an average temperature of 22◦C ≈ 72◦F, which seems rather low given agronomic research showing that plant growth is linear in temperature up to about 32◦C. There are other anomalous results, but as the coefficients are not significant, we do not discuss them further here. The results of the degree days model are very reasonable in the light of the agronomic literature. But how robust are they across plausible alternative specifications of variables and data? Here we briefly describe several sensitivity tests. A more complete discussion is given in the appendix available on request. We test the stability of the five climatic coefficients across the several census years in our pooled model. During this period there were some significant changes in farmland values east of the 100th meridian; the overall farmland value in this region declined by 32% between 1982 and 1987 in real terms, and increased by 13% between 1987 and 1992, and 14% between 1992 and 1997.

Changing hydrologic trends throughout California will directly impact San Diego’s water supply

Offering relief from the consistently hot, dry, drought conditions of summer, CLCF play an important role in hydrological regime and ambient temperature . Although CLCF are a distinct component of San Diego’s climate, it relies on highly variable, complex factors. Thus, the net effect of climate change on CLCF remains uncertain . However, observational records exhibit that California CLCF has declined over the last decade, and that this decline can be attributed to urban warming . Future research on the response of San Diego’s CLCF to climate change is critical in understanding the implications for coastal and inland ecosystems and human communities.Despite intensified extreme events, it is likely that droughts will increase in both frequency and intensity . San Diego will experience more dry years as the subtropical zone expands and leads to a decrease in the number of wet days . More dry days will intensify already depleting soil moisture content. This will cause earlier spring soil drying and extended drying through the late fall into winter, and thus elongate seasonal dryness in California. The combination of longer periods of dryness, expanding subtropical zones, and warming temperatures, will lead to more dry years. With more dry years and dry antecedent conditions, it is projected that future droughts will increase in duration, severity, and frequency, which will also increase the region’s vulnerability to wildfire occurrence. The relative impacts of drought are likely to be more intense as well, as increased temperatures continue to create drier conditions. As the climate warms, drought conditions worsen, and Santa Ana wind events continue, it is likely that wildfire risk will also increase . Given San Diego’s water supply portfolio and its dependence on imported water,30 planter pot it is critical to consider the climate change impacts on the regions that supply much of San Diego’s water supplies.

These regions, largely Northern California and Colorado, are likely to experience changes in precipitation , temperature, and thus altered snow pack and runoff patterns .Water from Northern California, specifically snow pack in the Sierra Nevada Mountains, is expected to decrease due to higher rain and snow elevations, and earlier snow melt and spring runoff. Snow pack will be reduced by more than 60% by the middle of the century, with positive feed backs further exacerbating these warming and snow melt trends . Increased evapotranspiration and decreased snow pack will also cause decreases in water supply in the Colorado Basin . Over the next fifty years, droughts lasting five or more years are projected to occur fifty percent of the time. These impacts will reduce the water in these areas which supply San Diego, as well as counties across California and along the Colorado Basin, resulting in worsened water resource challenges. Thus, it is imperative that San Diego consider alternative sources of water and infrastructure developments, in addition to enhanced water-use efficiency across all sectors.As a sector that is greatly dependent on climate and highly sensitive to environmental conditions , agriculture is exceptionally vulnerable to the effects of a warming world. With ongoing shifts in natural processes that dictate agricultural practices, productivity, and costs, the future of agriculture is one with distinct and palpable challenges. Because the effects of climate change on agriculture are highly dependent on variables such as climate, geography, soils, and customary agricultural practices, the net impact felt by regions will vary greatly. In some areas, it is projected that climate change may result in beneficial consequences for agriculture, while in others, consequences could be detrimental. Therefore, it is necessary to develop regionally and locally unique solutions for these changes . Most Mediterranean regions, such as San Diego, will feel the greatest impacts from increasing variability in precipitation compounded with increasing temperatures. Precipitation and temperature are deeply embedded within the hydrologic cycle, and thus, as these climate variables continue to shift, they will alter many hydrologic processes. Furthermore, increased climate variability making adaptation increasingly difficult for the farming community .

Ongoing changes, such as the timing and frequency of precipitation, reduced snow pack levels, and earlier snow melt, present several challenges for the region’s water resources . These water-related challenges are inextricably linked to the overall functioning and viability of agriculture, and are thus paramount in determining the persistence and growth of San Diego agriculture. There are several key hydrologic variables that play a role in the overall functioning of a landscape, and as these hydrologic variables change, agricultural lands are impacted. Table 2 outlines these hydrologic variables, their impact on a landscape, and projections for future climate scenarios. These hydrologic variables include: CWD, AET, runoff and recharge. One of the major hydrologic variables San Diego’s landscape is climatic water deficit . CWD is the amount of additional water that would have evaporated/transpired if soil water was not limiting, combining the effects of evapotranspiration, solar radiation, and air temperature on watersheds, given the soil moisture level from precipitation . CWD can be translated to direct impacts for agriculture. In Mediterranean climates, it is considered a proxy for water demand based on irrigation needs . Another important hydrologic variable that heavily impacts landscapes is actual evapotranspiration , which is the amount of water actually lost by the vegetated surface. For the farming community, AET translates to above ground net primary productivity and is used as a proxy for productivity of a landscape . Changes in AET and CWD can be used in quantifying the additional water necessary to maintain vegetation or crops in a landscape , effectively identifying the amount of irrigation demand needed to cover seasonal deficit . Critical to the relationship between climate change and natural landscapes is understanding the contribution of agriculture to increasing GHGs and in turn, climate change. Degrading and eroding soil from intense grazing, plowing, and clear-cutting, has throughout time, played a significant role in the increasing concentration of atmospheric GHGs . Long-term degradation of important features of natural lands, such as soils, forests, and wetlands, is one of the key drivers of a warming world . Relative emissions and impact, however, vary with region depending on soil properties and agricultural practices. In the San Diego region, agriculture contributes approximately five percent of total unincorporated county emissions . In general,plastic planters bulk most farm-related carbon dioxide emissions result from a variety of soil, livestock, and manure management practices, including soil tillage, overgrazing, farm equipment, livestock and fertilizer use .

The world’s soils play a critical role in food production, water resources, both quantity and quality, and increased net primary productivity. Enriched with soil organic matter , soil has the ability to recycle dead matter into mineral-rich nutrients vital for plants and other organisms. Additionally, soil provides the distinct and critical service of removing gases from the atmosphere. Through the biological process of carbon sequestration, carbon dioxide is removed from the atmosphere and stored as sinks in soils. This service helps keep terrestrial and atmospheric carbon levels in a balance . Carbon is the primary component of SOM and provides soil with defining characteristics such as water-retention capacity, filtering capabilities, structure, and fertility . Because pools of soil organic carbon aggregates are stable and robust, they provide the largest store of terrestrial carbon and have the ability to be sequestered for up to a millennia . The length of time and amount of carbon that remains in the soil is largely influenced by ecosystem and environmental processes, depending on vegetation, soil properties, water drainage, and climatic conditions. Thus, levels of SOC varies on large-scale global patterns and on smaller-scale regional and sub-regional basis . The unique capability of soil to nourish vegetation and capture carbon long-term helps buffer the implications of climate change for both society and ecosystems alike. Additionally, with the likelihood of increased flood events, agriculturally managed lands could play a role in retaining flood waters for flood risk reduction as well as possible groundwater recharge. However, the ability of soils to provide these services is contingent on its overall quality. The length of time and amount of carbon that remains in the soil is largely influenced by management practices, in addition to ecosystem and environmental processes . If soils are poorly managed with unsustainable agricultural practices, soils can release CO2, contributing to atmospheric concentrations. Alternatively, if healthy, soils can enhance sequestration and continue to play an essential role in climate change mitigation. Thus, promoting healthy soil is critical to ensuring the resilience of landscapes, agriculture, and society. Recently research has focused on the potential of enhancing SOC sequestration to help moderate high levels of atmospheric carbon. On a large scale, SOC sequestration could hypothetically sequester all current annual GHG emissions globally, at approximately 52 gigatonnes of CO2 equivalent . This research highlights the ability of soils to offset increasing atmospheric CO2, where restored SOC pools could promote productivity, fertility, and resiliency to a variety of climate extremes. There are a variety of carbon farming practices that farmers can adopt in order to achieve these benefits .

Table 6 outlines some of the common on-farm conservation practices recognized by the Natural Resource Conservation Service to improve soil health, sequestration rates, and associated co-benefits . From permanent crops, compost and mulch application, windbreak renovation, no-till row crops, to cover cropping, the agricultural community has several options when it comes to implementation . Overall effectiveness, in terms of sequestration rate, GHG reduction, and benefits will depend on various climatic and environmental factors. Thus, suitability of practices vary by region and individual agricultural context. Many studies have shown that compost application is one of the most impactful practices for carbon sequestration rates. Field and model results from a report within California’s Fourth Climate Change Assessment indicate that a one-time ¼ “ application of compost to California’s range and croplands can lead to increased carbon sequestration and net primary production rates in soils maximized after 15 years . In another study conducted by the Marin Carbon Project, it was shown that a one-time application of a ½ layer of compost on grazed range land was able to increase carbon storage by 1 ton of carbon per hectare. This resulted in both increased forage production and water holding capacity . This study uses down scaled statewide modeling data to analyze hydrologic response in San Diego as it relates to agricultural land evaluation, based on a report for California’s Fourth Climate Change Assessment by Flint et al. 2018. The Basin Characteristization Model is a grid-based model that combines climate inputs, watershed, and landscape characteristics to calculate the water balance. By combining fine-scale data, the BCM can generate detailed assessments of coupled climate and hydrologic response . Precipitated water can act in various ways as it enters into a landscape, from evaporation and transpiration, recharge, or runoff. Given climate data, governed by latitude, longitude, elevation, slope, and aspect, in addition to soil properties, and characteristics of deep soil materials, the BCM can effectively model the response of these hydrologic factors . Flint et al. 2018 utilized a revised version of the BCM to include SOM percentage for calculations of WHC. Using this modified version of the BCM, Flint et al. calculate how increases in SOM changes hydrologic response to climate . The study assesses changes in WHC as a result of additional SOM, and the impact that changes in WHC have for hydrologic variables such as recharge, runoff, AET, and CWD throughout the state. Table 6 outlines the predicted hydrologic response to these changes, however, response is dependent on several factors, including precipitation. Figure 10 shows the high variability of potential hydrologic benefits from increases in SOM for the period of 1981-2010 across the state’s working lands . These results showcase the diversity of climates and soil properties throughout the state’s landscape, and the impacts these factors have for potential benefits in forage production, landscape stress, irrigation demand, and water supply .Hydrologic benefit is calculated using a hydrologic index from changes in these variables, specifically increases in AET and recharge, and decreases in CWD . The hydrologic index is binned into three classifications of benefit based on index value, including “no benefit”, “minimum benefit”, “moderate benefit”, and “maximum benefit”. Hydrologic benefit is mapped for the entire county , the unincorporated county excluding the incorporated areas , and the incorporated county .

Passive RFID sensors have a relatively short range compared to other communication protocols

Healthy soil is rich with microbial life, and over time, the microbial communities will adapt and digest what is more likely to be available to them. As an important note for polymers – when a polymer is described formally as a ‘biodegradable polymer,’ it contains hydrolyzable bonds – meaning they are affected by hydrolysis . Therefore, their most crucial degradation mechanisms are chemical degradation by hydrolysis or microbial/enzymatic digestion. The latter effect is often referred to as bio-degradation, meaning that the degradation is mediated at least partially by a biological system. Our strategy for controlling the degradation rate of our device is to apply both principles of passive geometry and material selection. We make devices out of ‘shells’ of materials that degrade at different rates. More specifically, we paired fast degrading printed conductors with slow-degrading, wax-based encapsulation that degrades uniformly by surface erosion. Figure 5.15 describes the performance of such a device over time, with cross-sections at critical intervals in the degradation process. Material selection was determined by literature review and experimentation. Lee et al. have investigated the use of electrochemically-sintered zinc in a water-soluble polyvinyl propylene binder as a naturally-degradable printed conductor material. Meanwhile, natural waxes have an exciting opportunity as naturally degradable encapsulation material. They have been able to retain the operation of underlying degradable electronic systems for weeks to months. Figure 5.16 shows the accelerated degradation of wax blends held at elevated temperatures in an incubation chamber over 28 days.Unfortunately, it is impractical to make a nitrate sensor node 100% degradable. For example, the ISM,grow blueberries in containers which provides the operating mechanism for the nitrate sensor, necessitates a hydrophobic polymer backbone to function.

Because of this, it is impossible to make this component naturally degradable by the current mode of operation. Fortunately, the mass of this component is minimal – only about 0.5 mg. To put that into perspective, it would take 10,000 ion-selective membranes to produce as much plastic pollution as a single credit card. Table 5.3 shows all of the components in a wireless nitrate sensor node and what naturally degradable materials they can be substituted with.Some components of a conventional wireless sensor node are difficult or even impossible to replace with naturally-degradable materials, as shown in Table 5.3. For example, degradable batteries or other energy storage devices exist in literature, but none are resilient or low-cost enough for our application. Similarly, using onboard energy storage and harvesting necessitates a higher complexity micro-controller, which corresponds to larger and more costly micro-controllers. One method of circumnavigating these components is using passive sensor nodes, such as passive RFID sensors. Passive RFID sensors comprise an antenna, an RFID IC, and a sensor. Of note, there is no onboard energy storage, meaning an external power signal must be sent to the node to take a measurement. In the case of RFID, an RF signal is transmitted by an external RFID reader. The antenna receives the wave and transduces it into an electric signal which ‘wakes up’ and powers the RFID IC. The RFID IC acts as the micro-controller, communications IC, and power management. When it receives the wake-up signal, it uses the power in that signal to read the sensor and modulate a return signal through the antenna to the reader corresponding to the sensor measurement. By designing a sensor node using this passive RFID scheme, we estimated that we can make the naturally-degradable nitrate sensor nodes 99.99% degradable by mass. Ag/AgCl strips were fabricated using the same parameters with Engineered Materials Systems, Inc. CI-4001 ink. Afterward, they were cured in an oven at 120C for two hours. After curing, the carbon and Ag/AgCl strips were cut into six equal-sized electrodes. Each electrode was then sandwiched between two patterned wax sheets and heated in an oven at 55C for thirty minutes. The wax sheets were made by soaking untreated plywood sheets in water before dipping them in molten wax and removing the waxy film that forms on the surface. The thin water layer on the surface of the saturated plywood sheet acts as a barrier to the hydrophobic wax, allowing for easy removal. The thickness of the wax sheets was controlled by dipping the saturated plywood sheets multiple times in quick succession, obtaining wax sheet thicknesses of 350 µm, 700 µm, and 1.25 mm for one, two, and three dip cycles, respectively.

The wax sheets used for encapsulating the bottom of the sensors were used as-is, while the sheets used for encapsulating the top of the sensors had 12.5 µm windows for the membranes removed using a laser cutter. An image of an ISE immediately after the encapsulation step is shown in Figure 5.17B. ISE membranes were fabricated by mixing 5.2 wt% Nitrate Ionophore VI, 47.1 wt% dibutyl phthalate, 0.6 wt% tetaroctylammonium chloride, and 47.1 wt% PVC. A total of 0.2 g of this mixture was dissolved in 1.3 mL of THF. 180 µL of the membrane solution was drop-cast on the ISE surface and dried in a fume hood for 15 minutes. The REs employed a CNT transducer layer between the Ag/AgCl electrode and the membrane. This transducer was composed of 0.01 g of CNT and 0.05 g of F127 -block-poly-block-poly diacrylate dissolved in 10 mL of THF, which were sonified for 1 hour in an ice bath using a Branson Digital Sonifier probe. 120 µL of the resulting transducer cocktail was deposited onto the RE surface. The salt membrane was made by dissolving 1.58 g of Butvar B-98 , 1.00 g of NaCl, and 1.00 g of NaNO3 in 20 mL of methanol. The mixture was sonified for 30 minutes in an ice bath, and 180 µL of the resulting salt membrane cocktail was deposited on top of the CNT transducer. Unless otherwise noted, all chemicals used in ISE and salt membranes were obtained from Millipore Sigma. After each electrode was made, they were cold-sintered to 22 AWG wire using 8331D silver conductive epoxy and en-capsulated with multiple layers of Gorilla 2-part Epoxy . Figure 5.17 shows an image of the fabricated naturally-degradable nitrate sensors. Wireless sensor networks are becoming more and more relevant in agriculture. Researchers have made agricultural WSNs to monitor weeds, evapotranspiration, crop disease, and water use. However, there are limited examples of agricultural WSNs for monitoring nitrate. The design of a wireless sensor network in agriculture has a host of unique challenges. Issues like energy consumption for autonomous operation of sensor nodes dictate design and development issues, including communication protocols and deployment. Furthermore, the placement of sensor nodes in open, uncontrolled environments presents another host of unique challenges, such as damage accumulation from weather or wildlife. Finally,blackberry plant pot the scale it takes to implement WSNs in agricultural settings is much larger than in commercial or industrial environments. Cropland accounts for about 11% of the habitable land globally, and in the United States, the average crop farm is 445 acres. This dictates the placement and quantity of sensors needed, as discussed in Section 5.2, and shows that large numbers of sensor nodes are required. Different researchers have adopted different strategies for circumventing these challenges. Ding and Chandra investigated using Wi-Fi for measuring soil moisture and electrical conductivity.

Syrovy et. al. utilized Long Range, Wide Area Network communications to transmit data from paper-based soil moisture sensors. Yu et al. deployed a system where the sensors connect directly to a person’s phone over Bluetooth Low Energy. Here, we propose an agricultural WSN explicitly designed for the precision management of soil moisture and soil nitrate. The naturally-degradable nitrate sensor nodes demonstrated in Section 5.4 can be deployed at minimal cost and without the need for maintenance throughout any agricultural field using the techniques outlined in Section 5.2.Hence, a reader needs to be brought to within a few meters of the sensor to sample data from the sensors. Because many sensors need to be distributed across an agricultural field to acquire granular enough data to capture soil variability, drones offer a unique advantage over other existing methods to sample data from the sensors. With drones and drone accessories becoming less expensive, using multiple drones to simultaneously map sensors has become an attractive route to efficiently gather data. Machine-learning algorithms are a promising approach for generating flight path maps due to their ability to solve highly non-convex problems quickly, and even operate in real-time as a digital twin. We developed an agent-based dynamics model to generate flight paths for the drones to scan each sensor in the field while circumventing obstacles and avoiding crashes. The coordinated effort of multiple drones working towards a common objective has similarities to swarms found in nature, such as bees and ants, where the accumulation of each agent’s actions and reactions can give rise to phenomena and emergent behavior where the system becomes more than the sum of its parts. Unlike bees and ants, it is atypical for a drone swarm to contain a ‘leader.’ In the context of field mapping, the drone swarm adapts to changes within the system, such as the disablement of a few drones due to collisions or other unforeseen causes. We developed a robust agent-based model capable of optimizing the flight paths of each drone within a swarm to scan all sensors within a simulated agriculture field. The simulations determine each drone’s aerial route for optimal flight path planning. Each drone within the simulated framework – an ‘agent’ – has its own characteristics that determine how it interacts with its surroundings, such as its environment and other drones. These characteristic parameters take inspiration from the physics of molecular dynamics, where each agent is modeled as a point mass particle that is attracted and repelled by other objects within the system. A genetic algorithm determines the direction of propulsion. The framework inputs are the field’s shape, the number of agents, and the positions of sensors . This framework can be used for various sizes and shapes of agriculture fields. Depending on the field geometry and the locations of sensors within that field, the framework will output several suggestions of each drone’s flight path trajectory. AUTONOMOUS agricultural mobile robots become increasingly more capable for persistent missions like monitoring crop health and sampling specimens across extended spatio-temporal scales to enhance efficiency and productivity in precision agriculture. An autonomous robot needs to perform certain tasks in distinct locations of the environment subject to a specific budget on the actions the robot can take . During in-field operations, the actual costs to complete tasks can be uncertain whereas expected costs may be known. Also, some tasks can be more urgent than others, and have to be prioritized. It is often the case that there exists some prior information about a required task that can bias robot task assignment. Hence, it is necessary to develop approaches that utilize limited prior information to plan tasks with uncertain costs and priority level. There exist two key challenges for efficient robot task allocation in precision agriculture. First, prior maps can indicate biases in task assignments, but may not be trustworthy. This is because conditions in the agricultural field can change rapidly, are dynamic, and may be hard to predict ahead of time. Second, as the budget is being depleted, the robot needs to periodically return to a base station . Addressing these two challenges simultaneously poses a two-layer intertwined decision making under uncertainty problem: How to perform optimal sampling given an approximate prior map, and how to decide an optimal stopping timeto avoid exceeding a given task capacity? This paper introduces a new stochastic task allocation algorithm to balance optimal sampling and optimal stopping when task costs are uncertain. A direct approach for persistent sampling is to survey the entire space and perform the desired task sequentially. The main drawback is that the robot would then exhaustively visit all sampling locations without prioritizing those that would yield a higher gain or would be more time-critical. Orienteering can address part of this drawback by determining paths that maximize the cumulative gain under a constant budget. The robot prioritizes visiting adjacent locations if they jointly yield higher gains than isolated high-gain locations, and provided that any budget constraints are not violated. However, this strategy can be insufficient for missions where some tasks are more urgent than others. For instance, several existing robot task allocation strategies, albeit for distinct application domains, typically consider a deadline or user-defined importance levels.

The low sensitivities and relatively low R2 values are less than those found in sand or clay soils

While circle-packing nearly describes our model problem, there is one major caveat: no physical justification prevents the circles from overlapping one another. This ’soft boundary’ makes it possible to achieve 100% coverage of the domain by allowing overlap. If the only objective was to maximize the effective areal coverage of the field, then one could distribute sensors next to one another without discretion. However, the monetary cost of sensors, sensor operation, and sensor maintenance makes this approach unreasonable, which motivates our stated objective of maximizing field coverage with the fewest number of sensors possible.The outcome of this algorithm is that sensors are placed throughout the field such that sensors are placed in the largest gaps between sensors. This is done by incrementing the acceptable distance between sensors by a small amount and then making many attempts at placing a sensor before repeating the process. We score the fitness of each placement design by the ratio of the number of field pixels that are within the half-variogram range of a sensor to the total number of pixels in the field. In other words, what percentage of the field area is within the half-variogram range of one or more sensors? This process is repeated until it is impossible to place a sensor outside of the range of all other sensors in the design, or until the field is completely covered. The flowchart for this algorithm is shown in Figure 5.3A, and a schematic depicting the evolution of sensor placement in an arbitrary field shape is shown in Figure 5.3B.Soil is a complex environment. It is a three-phase medium containing organic matter, minerals, metals, ceramics, gases, water, and a host of biological life. This is all to say that many things could complicate potentiometric nitrate sensor readings. Although the nitrate sensor nodes are sensitive to nitrate and are largely insensitive to most other ions – soil is more complicated than aqueous solutions. Ideally, nitrate sensors should not be sensitive to soil properties ,plastic garden pot but calibration or direct measurement of the interfering property could help return accurate nitrate measurement values.

To characterize the performance of the nitrate sensor nodes in soil, the potential of the nodes was recorded over time in varying nitrate concentrations and moisture levels in three types of agricultural soils. After calibrating the nitrate sensor nodes in aqueous solutions, they were immediately cycled through several containers of soil saturated with aqueous solutions of varying nitrate concentrations. Finally, they were measured in several containers of dry soil with varying volumes of 10 mM nitrate solution. The nitrate sensor nodes were tested in sand, peat, and clay soils. Sand tests were performed with commercially available desert sand , consisting of only sand-sized soil particles and no initial nitrate concentration. Clay tests were performed with an agricultural clay soil utilized for perennial alfalfa and from Bouldin Island in the Sacramento–San Joaquin Delta, California . Peat tests were performed in Miracle-Gro Nature’s Care organic potting mix. To circumvent the possibility of hysteresis attributed to the exposure of one soil type before measuring the sensor node in another soil type, the sensors were only measured in a single kind of soil on a given day, and the stakes were gently but thoroughly cleaned with deionized water and dried between measurements. In the nitrate concentration experiment, aqueous solutions of 0.1 mM – 1 M NaNO3 were used to saturate an array of containers containing sand, clay, or peat soils, depending on which soil type the sensors were being tested in that day. After saturating the soil with a surplus of nitrate solution, the sensor nodes were placed in the 0.1 mM container by displacing the soil with a spoon, inserting the stake, and then gently redistributing the soil over the electrode surfaces. After the nitrate sensor node was inserted into the container, the program ran on the micro-controller, uploading the recorded potential over WiFi to an online spreadsheet once per minute. Once the potential of the sensor stabilized, it was then moved to the 1 mM container, and so on. Figure 5.11 shows the nitrate sensor nodes being measured in the sand. After the measurements were made, the nitrate concentration of the soils was determined by taking KCl extractions and then measuring the extractions with ionexchange chromatography. The nitrate sensor nodes’ responses to the calibrated nitrate concentrations are plotted in Figure 5.12.

We expect similar results in measuring the sensor nodes in saturated sand as we would in aqueous solutions. This is because sand has a low cation exchange capacity, so few other ionic species are present. Furthermore, uncharged solids in the soil are unlikely to interfere with the potentiometric measurement.The nitrate sensor nodes in the sand showed strong linear relationships between nitrate concentration and recorded potential, with about half of the 17 sensor nodes included in this trial showing R2 values above 0.99. Eight sensor nodes with R2 > 0.99 were used for the remaining analysis. The average sensitivity for these sensors was -42 ± 8 mV/dec. Figure 5.12A shows the linear relationship between nitrate concentration and output potential for five of the sensors in the sand, with sensitivities ranging from -40 to -42 mV/dec. In clay, five of 14 sensors had R2 values > 0.9, and the sensitivity of the sensor nodes was -39 ± 8 mV/dec mV/dec, shown in Figure 5.12C. Clay has a much finer grain size and higher cation exchange capacity than sand. This likely explains the loss in sensitivity when measuring in clay compared to an aqueous solution or saturated sand. This suggests that soil texture alone is not a primary interfering factor in soil nitrate measurements, though other soil characteristics may be. If this is the case, calibration of the sensor in different soil types would be necessary to deploy such devices in agricultural applications. Figure 5.12B shows the output potential of three nitrate sensor nodes in peat soil at varying nitrate levels. The nitrate sensor nodes demonstrated sensitivities of -31 ± 8 mV/dec,draining pots with R2 values for each sensor’s best fit line of 0.8, 0.71, and 0.99.We suspect this is because the sensors had become damaged by the time of these measurements. The sensors were measured in sand and clay soils before the measurements in peat, so by the time of these measurements, the sensors had been inserted, removed, and rinsed from soil media many times, and it is possible that despite our best efforts, the sensing element may have become damaged. Compared to Figure 4.10, which plots ‘fresh’ gold electrode nitrate sensors in the same soil type measured with a Campbell Scientific data logger, the sensitivity was -47 mV/dec, R 2=0.95, and E0 variation 30 mV . The different results depending on the age of the sensor and the electronics used to measure it highlights the importance of improved stability for real-world use cases.

To find the impact of moisture content on the sensor nodes, 10 mM nitrate solution was used to water an array of containers containing sand, clay, or peat soils, depending on which soil type the sensors were being tested in that day. The containers were watered to 0 – 50% volumetric water content in 10% increments. The sensor nodes were initially placed in 0% VWC soil while the program ran on the micro-controller, uploading the recorded potential over WiFi to an online spreadsheet once per minute. Once the potential of the sensor node stabilized, it was then moved to the 10% VWC container, and so on. Ideally, the sensors’ output signal should not depend on soil moisture content. However, potentiometric sensors require ionic contact between the two electrodes to function: ions must be able to move freely between the ISE and RE, proportional to the finite current associated with potentiometric measurements. If soil does not hold enough water to support the flow of ions, the sensor becomes an open circuit, and there would be no signal. The results are shown in Figure 5.13. The potential abruptly increases at low moisture content because the micro-controller is programmed to report high signal output at open-circuit inputs. However, above a certain moisture threshold, sand and clay soils follow the expected pattern: relatively constant potential with respect to moisture content. For sand, the threshold is between 10 and 20% volumetric water content VWC, while for clay, it is between 20 and 30%. This makes sense because clay’s matrix potential is higher than sand’s, meaning water is bound more tightly to the surfaces of solid particles in clay, lowering the likelihood of an ionic pathway forming between the ISE and the RE of the nitrate sensor. The relationship between water content and sensor signal output in peat soil is less clear. The minimum water threshold seems to be between 10-20%, but the output potential is not as stable between 30-50% VWC. This could be attributed to the possibility of damage buildup in the nitrate sensors, or it could indicate that different sensors have different minimum thresholds. It could also be explained by the variations of water retention in high-organic matter soil. Further investigation is warranted.As noted earlier, there is significant E0 variability from one sensor node to another. Up until now, the behavior of the nitrate sensor nodes has been plotted altogether. However, when we look at individual sensors, E0 is relatively consistent, indicating that the variability has to do mainly with the sensor nodes rather than the properties of the soil. Individual sensor nodes have relatively consistent E0 values across the different trials. Subsets of the same 20 sensors were used in the six plots shown in Figures 5.12 and 5.13. The sensors with low R2 in the sand also had low R2 in clay, while most sensors with high R2 in the sand also had high R2 in clay . Similarly, sensors with relatively high potential outputs had this characteristic across soil types and nitrate/moisture measurements. Figure 5.14 shows the response of two sensor nodes measured in sand and clay soils. Sensor A has a much lower potential than Sensor B in all cases. A common misconception surrounding ‘green materials’ is that if something will decompose or degrade into a naturally-occurring material , it is safe to deploy into the environment. This is an incomplete way of thinking because, by that definition, every material on the planet is ‘green.’ Stop reading for a moment and take a look around you. Everything you see would eventually break down into naturally-occurring materials on a long enough time scale devoid of human intervention. It was made of materials sourced from our planet, after all. The time it takes for something to degrade is also essential and is the basis of many different standards surrounding bio-degradation. Different organizations worldwide have developed standards that benchmark different degrees of how things degrade. Table 5.2 highlights some of the more widely known standards. Interestingly, these standards vary widely in the description, and only the compostability standards explicitly state the timescale at which those materials degrade. The standards in Table 5.2 also lack to describe of how these materials might degrade. To design devices that degrade in a controlled manner, one must first understand the different ways a material might degrade. There are three ways in which materials degrade. These are compositional or micro-structural changes, time-dependent deformation and associated damage accumulation, and environmental attack. Generally, materials degrade by some combination of several mechanisms. There are few opportunities for time-dependent deformation in soil, though several mechanisms catalyze compositional changes or attack material bonds. These primary mechanisms by which materials degrade in soil are discussed here. For most materials, microbial activity is the primary contributor to the degradation rate. Microbial and enzymatic digestion describes the degradation carried out by microorganisms and naturally-occurring enzymes. Microbes can degrade most – if not all – naturally occurring organic chemicals and convert them to inorganic end products to supply the microbes with nutrients and energy. As with other life forms, the material’s molecular bonds are broken to release energy, transformed into a less thermodynamically energetic material, and excreted from the microbe’s system. Intuitively, different materials degrade at different rates. Generally, more complex molecular structures degrade on longer time scales than simpler ones. Also, different microbes and enzymes are preferential to different molecular compounds.

New methods for evaluating uncertainty also can be used to devise model simplification strategies

A holistic upscaling from the point source to the landscape scale requires incorporation of several interacting, complex components, adding substantial complexity above and beyond the agricultural system itself. Thus, a major consideration in environmental modeling is how to best capture essential interactions while maintaining models that are feasible to implement with available data and computational resources. Fig. 4 illustrates the various components linking point to landscape scales. A first element for the linkage from point to landscape is estimation of surface and subsurface fluxes and ecological transitions along the lateral scale. Coupling with landscape microclimate models provides the vertical inputs used by the agricultural systems models, as well as gradients along the landscape. Coupling with hydrological models provides water flow paths like surface runoff, vertical and lateral groundwater flow, and interactions between vadose and groundwater zones and with adjacent surface water bodies . Water quality models provides sediment and solute transport along the landscape controlled by water flows , and other effects like wind erosion. Integration and upscaling of landscapes into the watershed scale requires 3-dimensional coupling of the surface and subsurface water, energy and mass transfers. At this scale, the groundwater aquifer system typically transcends the boundaries of the watershed and necessitates analysis at the regional scale to evaluate not only the impacts of the cropping and animal production systems on water quantity and quality, but also feed backs from the hydrological system in the agricultural system . Further, mesoscale rainfall and evapotranspiration distribution models control the local surface and subsurface flow intensities,hydroponic pots pollution and abatement. At this scale, human effects through land-use changes as well as ecological dynamics and transitions on natural or protected lands are also an important and critical component to evaluate the overall sustainability of the agricultural system.

Current crop modeling upscaling approaches based on land use maps can be considered an efficient first-order approximation of the environmental linkages. For example, in the USA the US Geological Survey hierarchical system of Hydrologic Units Codes  is commonly used as the reference spatial mapping system to link spatially-explicit hydrological and crop yield simulations. Srinivasan et al. applied the Soil and Water Assessment Tool model to 8-digit subbasin HUCs in the Upper Mississippi River Basin and compared yields of the main crops with observed county-level USDA National Agricultural Statistical Survey data obtained for 1991–2001. SWAT uses spatially distributed watershed inputs to simulate hydrology, sediment and contaminant transport and cycling in soils and streams, and crop/vegetative uptake, growth and yields. Because many counties in the NASS database have missing data it was necessary to aggregate the crop yield data and simulation results to 4-digit sub-region HUCs . In general SWAT predicted crop yields satisfactorily over the long-term average for most 4-digit HUC , although errors greater than 20% were found for 14% of the HUCs studied. Further information on crop management may improve SWAT’s perform conclude that these errors stem likely from those predicting AET and soil moisture storage at these large aggregated scale, and “one could extend the validity and confidence in the model prediction of AET and soil moisture using a well-compared model on crop yield” . Thus, next generation models should consider the lateral connections through the landscape and regional scales to evaluate the sustainability of the integrated system, including effects on water and soil resources quality and quantity and ecological value. Although model complexity has increased in recent years and is a natural outcome of the proposed next generation integrated modeling, there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs .

New approaches have been proposed recently to evaluate the uncertainty-complexity relevance modeling trilemma , or to identify which parts of a model are redundant in particular simulations . Innovative approaches to simplify model outcomes to make them relevant in decision-making will be central to the next generation modeling efforts. For example, the identification of processes that do not influence particular scenarios, and the use of meta models, could allow simplification without affecting results .This thesis is divided into two sections. In the first section, a technical primer is given to provide a starting point for readers interested in sensor, printing, and machine learning technologies. In the second section, these three technologies are combined to demonstrate my dissertation work in developing nitrogen sensor nodes for precision farming applications. Grain growers apply on the order of a hundred to a few hundred pounds of nitrogen per acre, depending on the crop and field conditions. At a cost of tens of cents to a dollar per pound, with prices rapidly increasing in recent months, it is the second highest cost for many crops, outdone only by seeds. Nitrate fertilizer is conventionally applied uniformly across a field despite studies that have shown existing nitrate concentration in the soil can vary significantly on the order of tens of meters. Precision agriculture practitioners aim to designate site-specific management zones to direct more efficient nitrogen application, but the tools they have to gather data are limited. Optical remote sensing can be used to estimate nitrogen in growing plant material, but to get measurements of nitrate in the soil, a soil sample must be collected and taken back to a laboratory, for analysis via chromatography or spectrographic methods. Such measurements are highly accurate, but they are also expensive, labor-intensive, and give data for only one point in time and space. Nitrate is highly mobile, so concentrations change over time. Models can be developed to estimate nitrate fluxes based on measurements at the beginning and end of a season, but these rely on many estimations and assumptions. Environmental quality monitoring and precision agriculture require nitrate sensors that are robust enough to survive field deployment and soil insertion, can be mass-produced, and involve few or no moving parts. Additionally, the data must be simple to read. Printed solid-state potentiometric ion-selective electrode sensors have the potential to meet these criteria. The use of printing methods for sensor fabrication offers several advantages such as low cost, high throughput, and ease of fabrication.

In order to realize the benefits of printing and enable large-scale sensor deployment, both electrodes must be printed.Previous works have shown printed nitrate ISEs for use in aqueous environments and agriculture. Dam et al.demonstrated potentiometric nitrate sensors having a screen-printed nitrate ISE paired with a commercial RE for agriculture applications. Similarly, inkjet-printed nitrate ISEs were reported by Jiang et al. using a commercial Ag/AgCl reference electrode during measurements. In this work, we demonstrate fully printed, potentiometric nitrate sensors and characterized their sensitivity, selectivity, and stability. We then integrated the sensors into a wireless sensor node and characterized its sensitivity to nitrate concentration and moisture levels in the soil. We then replaced the components of the nitrate sensor node with naturally degradable components and characterized the devices. We propose a model-driven paradigm of measuring these sensors using swarms of UAV drones whose flight paths are optimized using machine learning. Finally, we demonstrate the need for sensor arrays to account for the interference that different analytes could cause,grow pot and provide preliminary results for a nitrogen sensor array that measures nitrate, nitrite, and ammonium concentration in aqueous solutions.A sensor is a device that is able to detect and measure some physical quantity of interest and communicate that information to another device or person. A common example of a sensor that you might recognize is the liquid-in-glass thermometer, shown in Figure 1.1. In this type of thermometer, a thin glass tube is filled with a small volume of liquid mercury that collects in a bulb at the bottom. Then, as temperature changes, the mercury expands or contracts in response, causing the peak of the mercury to move up or down the long stem. The stem, as you may know, is calibrated and marked with numbers corresponding to the temperature in Fahrenheit, Celsius, or both. In this example, the sensor detects the change in temperature by the liquid expanding or contracting in the glass tube. The temperature is measured and communicated to a person by the numbered ticks on the thermometer stem. A sensor should not be mistaken for a detector, which is able to detect and communicate a physical quantity, but fails to measure it. Consider for example a smoke detector, such as the one shown in Figure 1.1B. A smoke detector is able to detect whether or not there is smoke, but it doesn’t measure how much smoke there is. To a smoke detector, there is no distinction between a blazing house fire and overcooked salmon: both cause it to brazenly communicate its detection of smoke.Contrary to the thermometer example above, most modern sensors are electronic devices, which will be the type of sensor that will be discussed in this dissertation.

Many electronic sensors work by having some material that is sensitive to the physical quantity that is being measured, causing a property of that material to change with respect to the physical quantity. Other electronic sensors take advantage of natural laws, such as conductive metal wires arranged in a loop to measure the strength of the magnetic field that it’s in. Later in Section 1.2, we will go over the various types of sensors and the transduction mechanisms that a sensor might use. However, regardless of the mechanism or the physical property that is being measured, all electronic sensors have a sensing element that converts the signal of the physical quantity to an electric signal. As a quick aside, the opposite of a sensor is an actuator, which converts an electric signal into a mechanical action. Some common examples of actuators are electrical motors, hydraulic pumps, or pneumatic valves. Sensors and actuators are both transducers, which is a device that converts energy from one form to another. The distinction here is the intended purpose of the device: sensors measure and detect, while actuators perform an action. A more quantitative way of thinking of this is to look at energy conversion efficiency. The efficiency of energy conversion for sensors is immaterial because their purpose is to detect and measure. For example, if one sensor is 10% efficient at energy conversion but is less accurate than a second sensor that is only 2% efficient, then the second sensor is still an objectively better sensor of the two sensors because it is better at detecting and measuring. Contrarily, efficiency is an important metric for actuators because their purpose is to perform an action. An electric motor with a 2W load is objectively better than a motor with a 5W load, assuming they perform the same task equally well. The organization and classification of sensors vary throughout the academic literature and commercial marketplace. This is because there really is no perfect form of organization, as there are many ‘one-off’ devices that sense for some unique purpose or by some unique method. Further, there are many ways to categorize sensors: sensor specifications, sensor materials, transduction mechanisms, the quantity being measured, the field of application, whether the sensor is active or passive, direct or complex, and many, many more. It is analogous to classifying humans: humans can be classified by their age, gender, race, nationality, preferred sports team, favorite color, or the size of their ears. Similarly, sensors can be classified in many such ways.Sensors can also be classified as passive or active types. The distinction is simple: active sensors provide their own energy source to operate, while passive sensors use naturally available energy. An interesting example of both an active and a passive sensor is a camera. In a brightly lit location, natural light will illuminate the photographed subjects and then reflect toward the camera lens, where the camera simply records the radiation provided . In a dark room, however, there won’t be enough ambient light for the camera to record the subjects adequately. Instead, the camera uses its own energy source – the ‘flash’ – to illuminate the subjects and record the radiation reflected off them .

Public investments in infrastructure support major drivers important to industry success

We have observed the gradual weakening of the position of grower cooperatives and have noted in our stylized history that several have disappeared while others have had to deal with declining market share and financial challenges. Some aspects of mandated marketing programs have been problematic. Some programs have been terminated by grower referendums and others have suffered adverse court decisions in regard to quantity control prorate programs or assessment of the benefits of generic advertising to individual private label firms. The weakened competitive position of grower cooperatives and problematic features of mandated marketing orders are a consequence of the existence of large producers and integrated grower-processors of sufficient size to have market power of their own. This is now more common than it was in the 1920s and 1930s when enabling legislation was initially crafted. We believe that erosion in the contribution of co-ops and marketing orders will likely carry forward into the 21st Century.Population numbers and per-capita incomes are the dominant determinants of ultimate demand for the produce of California farms and ranches. Table 14 reviews California, national, and worldwide prospects for population and economic growth. Demand within the state grew over the epochs with significant increases in population and per-capita incomes occurring in the recent past. The relative growth in California demands will likely exceed that of nationwide per-capita demands in the future, the result of continued immigration and rising incomes. Export demands, important in the early history of the state, have again become important, responding to rising incomes in important offshore markets in Europe, Asia, and elsewhere. It is obvious that California agriculture, being demand driven, must be sensitive to changes that effect state, national, and international demands for the products of its farms and ranches. Issues will relate not only to quantities in trade channels but also to quality and supply reliability. Future marketing opportunities will be defined in importance by trade to both local and distant markets as well as the location of competitive battles for market shares. High export dependency for many of its products, increased in-state population’s demand for food products,pot raspberries slower growth in national markets, and, above all, the possibility of both growing populations and incomes in developing economies will be important determinants for success.

These two drivers reflect the most negative of our outlooks.The SWP, which was funded differently than the CVP , may provide a financial model for future endeavors to serve particular sectors of the state, including agricultural, urban, and environmental water users. Highways are in a deteriorating state. Increased maintenance and traffic congestion add to transportation costs. Local roads are affected by inadequate local funding. Airports and harbors also face difficulties, including the need for health and security assurances. “User pay” may also be the coming mantra for covering the costs of research, development, and extension services. Private agricultural R&D investments now exceed public expenditures, a trend that is sure to continue, possibly to the detriment of discovery of basic scientific research necessary for applied research products. It may also skew products toward large-market products, curtailing development of applied research products focused on smaller markets, e.g., for smaller-volume horticultural crops of the sort common to California. We have postulated that superior management will continue to be a hallmark of a viable agricultural sector in the future. Higher tuition costs reduce public contributions to each student’s education at the state’s colleges and universities. Here, too, the shift ap-pears to be one of user pay, perhaps reducing educational opportunities and, along with that, less public support of the tenet that the benefits of a well-educated population serve society and the general welfare of the citizenry. Extension and public-education programs are also under budget scrutiny with the almost inevitable consequence of reduction if not elimination. Private extension and public-education programs may be developed for those willing to bear the cost. Programs without a core, definable economic market may cease to exist.The increasing regulation of agriculture is driven by environmental, worker, and consumer safety issues, among others. There has been a continuous increase in regulations, compliance challenges , and the like. The majority of regulatory pressures have been imposed since WWII during a period marked by rapid increases in the number of people living in California and a growing slate of concerns by the general public about the environment, labor, health, and consumer policies. A recent study of farmer responses to the effects of regulations reflects one attempt to categorize the broadening scope of regulatory activity: employee-related regulations—safety and health, employee rights, disclosure, transportation; community-related regulations—consumer health and safety, community public health and safety; natural resource-related regulations—air quality, water quality, water rights, threatened or endangered plants or animals, and wetlands; and regulations related to transportation of materials—transportation of hazardous wastes and of goods and materials .

Regulations had a perceived effect on management practices, including those of employee safety and training, paperwork, technology, management support and improvement, cultural practices, scale of operations, and efficiency . We in no way argue that regulatory activities are not in the public interest, but they do increasingly change the policy and regulatory environment within which economic activity exists, constraining options, increasing costs, and reducing the competitiveness of California agriculture. We can admit only to viewing the future as one in which regulations will have profound impacts on firm and industry productivity and competitive performance.The second set of new drivers is the flip side to the positive impact of population and income growth on demand: namely, competition for natural resources. Urban growth has already pushed agriculture virtually out of Los Angeles, Orange, San Diego, San Mateo, and Santa Clara Counties and is now spilling over the Tehachapis from the south and the Coast Range from the west into the Central Valley. Thirty-five million people demand more recreation space, more water, more land, and more public space . When we recognize that only a small part of California is hospitable to human habitation, which, in general, occurs in the same areas where agriculture thrives, the potential for increasing abrasion on the urban-rural interface is inevitable. In summary, both drivers are responsive to the demands of a growing non-farm population in the United States and in California. Both are external forces to which accommodation must inevitably be made. Litigation is only infrequently successful in preventing negative impacts. Agriculture has come to learn to work with other interest groups to make the best of possible outcomes. To the extent that they limit choices of producers and processors, they can add to the cost of production, reducing economic profitability and placing California producers at a competitive disadvantage to producers in other states and even in other countries that are not similarly affected. U.S. markets for some crops may not be affected unless there are alternate producers of the same or substitute products in other states or if there are offshore producers with lower costs of production. Shares of market in third-country markets may be affected if there are global competitors in those same markets with lesser constraints or non-regulated production options.Willard Cochrane in his history of U.S. agriculture argues that agriculture in the United States has basically been “supply driven.” That is, production was initiated for self-consumption , but marketable surpluses emerged as productivity increased.

Contrary to Malthus’ prediction that demand would outrun supply, agriculture in developed countries has been characterized by production expanding more rapidly than demand , leading to oversupply, low prices, and, ultimately,plastic gardening pots government intervention to support incomes. The individual farmer’s main defense to such situations was to improve efficiency by adopting new technology. But if new technology was rational for one, it was rational for all, so aggregate supply expanded further, thus pressing prices to lower levels. The argument thus arises that agriculture is on a perpetual “treadmill” of overproduction and low prices . But California agriculture was not settled by small homesteaders intent on feeding themselves first and then possibly producing small surpluses of basic commodities—grain, milk, eggs, and meat. California agriculture started with big farms and ranches producing much more than could be consumed by the farmers directly. California farmers produced to meet someone else’s demand—for hides and tallow on the East Coast and in Europe, meat for miners and those supplying miners, wheat for export, nuts and dried fruits for the East and Europe, and so on. This dominant focus on meeting changing product demands, coupled with the range of total products possible, meant that California agriculture could be opportunistic. But to be so, it had to constantly adapt to survive and, yes, thrive. Constantly adjusting to changing opportunities has meant that California agriculture has a perpetual thirst for new technology—better and cheaper is always a potential market advantage. Being a long distance from markets for both outputs and inputs placed an extra premium on efficiency and adaptiveness. This set of factors pulled California agriculture through a quick sequence of changes that, as incomes climbed and population grew, meant that California agriculture became more and more diversified—200 crops in 1970, 350 in 2000. A lesser focus on basic crops meant that California agriculture has been less influenced by, or dependent upon, U.S. farm programs. However, if programs offered opportunities, California agriculture made the best of them. After all, an agriculture that is more efficient or productive than that of the rest of the country should be able to perform better. California agriculture has done so in cotton, rice, and dairy. Being less focused on Washington, California agriculture sought favorable state policies on water, transportation, research, and development, as well as favorable tax treatment. Until 1961, rural areas dominated the state senate. California agriculture was able basically to get its own way pre-WWII and remained a powerful force thereafter, at least until it lost the Peripheral Canal battles in the 1970s.

A few other distinctions will round out our case that California agriculture is different. It has always been a capital-intensive but simultaneously very seasonally labor intensive agriculture. California agriculture has always had a strong dependence on distant markets but, as its own state market grew, it adjusted to meet growing “instate” demands. It has benefited greatly from being in the middle of a rapidly growing and rich “domestic” market. Having access to 35 million local customers is preferable to having only 0.75 million or even three million . The constant adjusting to meet changing demands of affluent consumers has had consequences for the nature of California agriculture. Since 1952, the share of output accounted for by annual field crops has fallen precipitously while production of higher-valued vegetable and perennial crops has increased substantially. Dairy production now dominates the livestock sector. The result is that a rising share of California agriculture is on longer, multiyear production cycles. This necessitates a longer planning framework if periodic price run-ups are not to be followed by rapid buildups in production capacity, which inevitably result in market gluts and falling prices. This is currently happening in the wine industry worldwide.It is now time to end this story. We have consulted history. We have argued that California agriculture has performed well compared to U.S. agriculture. Based on the total value of crops and livestock marketed, California became the highest-ranking agricultural state in 1948. It has maintained that ranking ever since while increasing the difference between it and the second most important agricultural state . In 1950 California accounted for 8 percent of the total value of U.S. agricultural production. Since then, the share has steadily risen. In 2000 California agricultural production was worth $25.5 billion, amounting to 13 percent of the U.S. total. The value of California agricultural production of crop and animal products is now more than the combined value of the next two states, Texas and Iowa. But California agriculture’s dependence on federal government farm payments has been significantly less than that of the rest of U.S. agriculture . In 2000 California’s payments amounted to $667 million out of total U.S. direct government payments of $22.9 billion—only about 3 percent of the total. In contrast, Iowa received about 10 percent of U.S. payments and Texas received about 7 percent. It is likely that payments to California producers will fall relative to grain-belt areas because field-crop production will continue to decline as growers shift to higher-gross-income crops as markets permit.

Producers observed an increasing concentration of off farm processors and marketers

This epoch witnessed an eroding shift from a heavy reliance on production of undifferentiated commodities toward a more diverse, more specialized agriculture that responded more directly to consumer demands for food, fiber, and horticultural products. Beginning with an expanding production base in the San Joaquin Valley that was initially heavily devoted to field-crop production, California agriculture aggressively shifted over time toward higher-valued, more capital-intensive crops as markets permitted. The mass of production for many products shifted into the San Joaquin Valley from both the south and the north as markets expanded. Producers throughout the state scrambled to find opportunities that yielded acceptable economic returns to factors of production. The large shares marketed through cooperatives declined as producers apparently lost confidence that co-ops could make the transition to consumer-demand-driven marketing as efficiently as newer players focusing on more diversified market outlets for their products. Contractual arrangements and supply coordination increasingly replaced open or spot markets even for undifferentiated commodities.Some producers invested heavily to better integrate their operations vertically and horizontally to achieve economies of size and scope. The introduction to the state’s agricultural statistical summary for 1970 noted that “some 200 crops are grown in California, including seeds, flowers, and ornamentals” . The statistical report for the 2000 crop year reported a significant numerical revision, noting that “some 350 crops are grown in California,big plastic pots including seeds, flowers, and ornamentals” , nearly doubling crop numbers over the three decades.

The crops currently on the market reflect a much wider array of processed forms to better satisfy consumer and food-service institution demands. The increased number of commodities and product forms available reflected changes in the composition of both domestic and export demand. Domestic population increased substantially. Higher income, dual-income households demanded new product forms, and the growth of ethnic populations brought new crop demands, particularly from growing numbers of Hispanic and Asian consumers. Many consumers preferred and demanded convenience over even the most basic food preparation for many of their meals. Per-capita consumption shifts included changes in livestock demands and in the demand for more fresh, rather than processed, forms of many vegetables and fruits. Export markets also required different product forms than did domestic markets. By the end of the 20th Century, there were nearly 35 million people residing in California . One out of eight persons in the United States now resided in California, making the state’s diverse population an important, primary market for food and nursery products. The epoch began and ended with two contrasting water-resource scenarios that were also greatly influenced by population growth. Agriculture, which foresaw prospective ample quantities in the 1970s, now, in the face of resource competition from urban and environmental demands, was confronted with increasing water-resource scarcity and uncertainty at the turn of the century. Increased surface-water deliveries occurred following completion of Oroville Dam and San Luis Reservoir in 1967 and 1968, respectively, and with extensions of the California Aqueduct serving west-side and southern San Joaquin agriculture in the early 1970s. The Kern County Intertie Canal, which connected the east side of the valley with the aqueduct, was completed in 1977, signaling the state’s completion of major surface-water delivery systems. Even though there was a pronounced shift from field crops to higher-valued commodities in major areas of the San Joaquin Valley, the large increment in newly developed, better-irrigated lands served a total of 4.25 million acres of major field crops in 1970—a level even higher than that reported for 1950.

Later in the epoch, extensive crop acreage fell with the addition of more higher-valued crops. A second significant increment in surface-water availability was extension of the CVP’s Tehama-Colusa Canal, enabling intensification of production on the west side of the Sacramento Valley . Thus, California agriculture was flush with new surface-water supplies at the outset of this epoch. However, two of the century’s more severe droughts occurred during this period—the first in 1976–77 and the second over the period 1987–1992. The former was more severe, but the latter, longer drought had a far greater impact on agriculture. Both droughts sharply reduced water deliveries from the north to meet the growing needs of San Joaquin Valley agriculture. Average runoff in the Sacramento and San Joaquin hydrological areas fell to half of normal levels in the 1987–1992 drought. As a consequence, groundwater extractions in the San Joaquin Valley exceeded recharge by 11 million acre-feet during the 1987–1992 drought . At the end of the epoch, agricultural water supplies were reduced by new CVPIA requirements on CVP deliveries plus an inability to transfer supplies through the Delta due to environmental and physical system concerns even if surface water was available. The imminent reduction of Colorado River water supplies to the Metropolitan Water District of Los Angeles could also reduce surplus water supplies and create additional competition for moveable water. Water markets were developed during this period to facilitate the transfer of water among individuals and agencies in both annual and longer-term arrangements. But surplus water to serve future agricultural uses had evaporated from the system. Astute water management, including water transfers and water banking, was required in most agricultural regions by the end of the epoch.The early 1970s can be characterized as a period of aggressive expansion fueled by improving world markets and concern about “feeding a hungry world.” Product prices were strong for food commodities. U.S. producers were cheered on by Secretary of Agriculture Butz “to plant fence row to fence row,” promising the end of supply controls, long an integral piece of U.S. farm policy. With strong prices came a rapid run-up in U.S. farm asset values. The resulting increase in the value of farm assets fulfilled lenders’ security requirements for an increasingly capital-intensive, expanding California agriculture. Worldwide market demands collapsed later in the 1970s, but U.S. farmland values continued to rise into the early 1980s, in part due to negative real interest rates. Farmland appreciation, adjusted for inflation, over the period 1958–1978 was nearly 80 percent while common stocks lost 20 percent and cash lost nearly 50 percent .

Such information spurred substantial investments in U.S. and California farmlands by individuals, institutional investors, and even foreign investors, creating a price bubble that would collapse in the mid-1980s.Nationwide, the index of farm real estate values was 245 percent more in 1980 than in 1970. Because California agriculture had not benefited as greatly from rising basic commodity demands worldwide, the 1980 farm real estate value for California was only 110 percent higher than the 1970 value. Irrigated land increased more than non-irrigated land, and there were relatively larger increases in value in the San Joaquin Valley than in the Sacramento Valley. Some permanent plantings exhibited excessive land price escalation. Almonds and grapes were two permanent crops that attracted significant investment during the 1970s.Commodity Example – Almonds. Almonds were aggressively planted in the San Joaquin Valley beginning in the late 1960s. Non-bearing acreage amounted to more than 60,000 acres for all but two years from 1968 to 1982, and bearing acreage quadrupled from about 100,000 acres in the mid-1960s to 400,000 by the mid-1980s. Yields increased from three-quarters of a ton per acre to one ton and more. Exports expanded rapidly as supplies increased, accounting for about two-thirds of the crop by the end of the 1970s. The per-acre value of San Joaquin Valley almond orchards increased from $2,250 in 1970 to a peak of $8,570 per acre in 1983 before the investment bubble burst. Within five years, the average value for almond-orchards would fall by 40 percent to $5,200 per acre. Older marginal plantings in northern areas became uneconomical and were removed,large plastic garden pots further accentuating the shift of production to the San Joaquin Valley. Total bearing acreage stabilized in the range of 400,000 to 430,000 acres from the mid-1980s to the mid-1990s. Commodity Example – Grapes. Grapes also attracted significant investments with most of the expansion also taking place in the San Joaquin Valley. The bearing, producing acreage of wine grapes statewide was between 120,000 and 130,000 acres for a long period—from the mid-1950s through the decade of the 1960s. As consumers expressed increasing interest in California wines, non-bearing acreage skyrocketed, amounting to 25,700 acres in 1970, 54,000 in 1971, 104,200 in 1972, and 149,000 in 1973. Most of the new non-bearing acreage in 1973 was in the San Joaquin Valley and in the emerging central coast wine-growing region . The statewide bearing acreage of wine grapes rose sharply from about 132,000 in 1970 to 318,000 by 1977. Another bubble arose. The peracre value of San Joaquin Valley wine-grape vineyards increased from $1,475 in 1970 to a peak of $9,770 in 1982 before a precipitous drop to only $4,000 by 1986. The appearance of surplus wine grapes also affected the fortunes of producers of Thompson Seedless grapes . Raisin vineyards had increased in value from $1,550 in 1970 to $10,840 per acre in 1980, but by 1986 their decapitalized value was also only about $4,000 per acre. Lesser-quality San Joaquin wine grapes proved to be of little interest to the wine industry given the increased supply of superior-quality grapes emanating mainly from coastal production regions.

Central coast vineyards rose in value and, after only a modest adjustment, rose further to more than $20,000 per acre by the 1990s. Prices also escalated in premium north coast production areas. By the end of the 1970s, substantial investments in perennial crops pointed toward the first of the epoch’s “ups and downs,” concluding with a mid-1980s collapse of land prices. Readjustment would affect producers across the length and width of the state.The decade of the 1980s began with the apparent over productive capacity of U.S. and California agriculture. Both were unable to respond to the loss of newly gained export markets and general weakening of world economic conditions following the energy price run-up of the mid- 1970s. Plus, some remaining groundswell from the 1970s continued in California as investment funds sought higher returns in agriculture, further contributing to unprecedented plantings of permanent crops. Commodity prices fell, input prices and interest rates rose, export demand turned down, and farm income declined. Even though it was evident that basic commodity prices were low, some apparently thought that California specialty-crop producers might be immune to agriculture’s declining economic fortunes, but that obviously was not to be. The farm financial crisis began in the Midwest but gradually affected all of U.S. agriculture, including California’s, where the impact was delayed and of lesser magnitude. Farm incomes fell in the face of high debt loads incurred in the land-buying and investment binges of the 1970s. Highly leveraged farms and farm investments were particularly vulnerable to sharp changes in economic fortunes. Consequences included rapid and deep decapitalization of assets, bank foreclosures of farms and ranches, and secondary and social impacts that permeated much of the economy. From 1982 to 1987, land values fell by as much as 60 percent in Iowa and Minnesota and by at least 40 percent in most Midwest and Great Plains states. California land prices fell by a lesser amount—28 percent on average. They would later improve for specialty-crop land but not for widely available field-crop lands that lacked higher and better use potentials. The mid-1980s was a period in which California agriculture sought to right itself from the fallout of the financial crisis. Lenders reevaluated behavior that had resulted in overextended lines of credit that had to be “worked out” following the crisis. Some producers maintained that credit was rationed, but lenders maintained that ample credit was available for applicants with portfolios reflecting appropriate credit risk. Cooperatives came under increasing pressure to yield economic returns commensurate with those of other outlets. Growers sought more immediate economic returns, in part to satisfy lenders’ operating loan requirements. Rising environmental concerns provided additional challenges regarding rice straw burning, use of chemicals, endangered species, and more balanced water use among agricultural, municipal, industrial, and environmental-use claimants. Structural adjustment within the processing sector occurred as older plants, many of which were located in urban and urbanizing areas in Southern California, the San Francisco Bay Area, and the Sacramento region, closed. Prices gradually rose and markets strengthened by mid-decade with rising domestic demand and expanded exports to Europe and Asia.