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Thirty-five concession plots were designated and allocated to farmers of the TFCGA

Compliance, monitoring, and surveillance are prioritized to minimize the environmentally degrading threats to the forests within the MGL, thus contributing to the Reducing Emissions from Deforestation and Forest Degradation initiatives of Belize. It is expected that the 31 farmers who gained rights to access to individual plots for cacao farming to be accomplished by the fifth year of planting. This has required an investment in materials, supplies, and capacity building for shade management and cacao pruning to enhance the health of trees to gain high-quality yields in a chemical-free environment, using natural agroecological measures. Cacao and other shade-loving fruit trees are planted in a setting mimicking that of a natural forest. This system addresses food security, as there is a high number of crops being cultivated within the land space where the concession has been granted. Biodiversity conservation is also enhanced since no hunting is allowed and the presence of fauna is being monitored to better understand how the integrity of the forest is maintained in a forest reserve with a management presence and intervention. The implementation of this agroforestry model aims at reducing the need to cut or clear more forested areas to plant crops, thus decreasing the expansion of the agriculture frontier.An effective internal governance structure is a key component of successful organized groups. This is perhaps one of the biggest hurdles to be overcome by TFCGA. Through the COL Program, Ya’axché has been able to provide ongoing sessions in decision-making, conflict management,10 plastic plant pots and strategic planning for the eventual autonomy of the forest community group. Great emphasis is being placed on developing the leadership and governance capacity by adapting best-practices measures. There is hope that in the near future TFCGA will become autonomous with a developed model that is easy to replicate in other forest reserves locally, regionally, and/or nationally.

Adapting alternative techniques can become challenging, as it requires breaking away from traditional practices—a behavioral change that must occur. In the 20 years of its existence, Ya’axché has built a strong relationship with eight communities in the MGL, based on respect, trust, and mutual understanding. The COL program at Ya’axché serves as the bridge between organized communities. This highlights the time extension officers invest in working closely with farmers to deliver technical support and materials in cacao-based agroforestry, beekeeping, and Inga alley cropping. Model farms using each of these climate-smart agricultural practices have been established and training sessions are delivered to other community members and groups, like TFCGA, using a farmer field school methodology approach. These model farms within the communities of the MGL are accessible for others to visit, increasing the probability of such models to be replicated. The strengthening of Indigenous communities equips them with the skills and tools to seek long-term investments. This facilitates opportunities in diversification to: access financial support to invest in climate-resilient practices; serve as model for the development of policies that will regulate cacao-based agroforestry; and gain recognition as a system that mitigates climate change impacts on communities and forests.Both protected areas and local communities are impacted by climate change and as such, there is always a need to be creative in overcoming this reality in communities where the impact is felt first-hand due to crop failure, flooding events, and drought. Creating alternative farming practices such as Inga alley cropping, a slash-and-mulch method implemented in the community agroforestry concessions, and apiculture will lead to climate-resilient communities that view protected areas as a source for livelihood improvements. A cacao-based agroforestry concession is now seen as a tool connecting forest communities to protected areas and including them in their sustainable use. Coordination and communication are the elements that have been prioritized at the grassroots level to influence a model of forest governance that is recognized by the regulating body, the Forest Department. TFCGA is governed by an executive committee composed of eight members with leadership roles and responsibilities. Having signed an articles and memorandum of association to be a legal community-based business group, capacity-building programs are elemental to strengthen TFGCA’s leadership and governance capacities to become a self-sustainable forest community group.

The group does not practice slashand-burn anymore and has embraced the guidance that Ya’axché continues to provide, in order to improve subsistence farming through guided measures that take into account the health of forested lands. Members of other communities pose a threat to the agroforestry concession since outside of the concession and forest reserve area there is no regulation of the use of pesticides. This can compromise crop production and its value-added status as being from a chemical-free area where agroecological practices are now prevailing. Inclusive dialogue has been strengthened as a response in conflict resolution to establish a buffer zone that will serve as a barrier between adjacent farmlands and the cacao-based agroforestry plots. The buffer zone is crucial to protect and conserve the integrity of the forest reserve as part of an integral block in the system of protected areas.Humanity has made giant strides toward eliminating hunger and malnutrition. Although continuous effort is needed to fight extreme poverty and hunger in some areas , today we produce more than enough food to feed the world adequately. In 2014, global cereal production reached a new record of 2.5 billion metric tons . Agricultural productivity growth has made substantial contributions to these successes. Since the start of the green revolution in the 1960s, agricultural productivity has experienced a consistent and rapid growth worldwide. For example, global land productivity, measured as an output of 185 crop and livestock commodities per harvested and pastured area, grew by a factor of 2.5 from 1961 to 2005, while labor productivity, the output per farmer, grew by a factor of 1.7 during the period . Global yield for maize, wheat, rice and soybean in 2007 was 2 to 3 times as large as it was in 1961 . These remarkable trends in productivity growth have taken place as a result of rapid adoption of, together with sustained improvements in, genetic technologies and agronomic management practices . Among them are plant breeding that results in improved hybrids and varieties, application of synthetic fertilizers and pesticides, and investments in irrigation infrastructure . Along with the successes of agriculture, however, came what Jonathan Foley terms the other inconvenient truth: “that we now face a global crisis in land use and agriculture that could undermine the health, security, and sustainability of our civilization” .

Indeed, agriculture has been identified as one of the major drivers of global environmental change, and is pushing the earth system beyond its safe operating boundaries . Through the intensive use of synthetic fertilizers and planation of leguminous crops, agriculture has critically disturbed the global nitrogen and phosphorus cycle, resulting in a wide range of environmental issues including eutrophication of lakes and coastal areas . Agriculture constitutes the single largest use of land, about 60 times as large as the area of all cities and suburbs combined , and poses the greatest threat to ecosystems . Irrigation accounts for 70% of water withdraws,plastic pot large contributing to water shortage and scarcity in many areas of the world . Further, agriculture is also the largest emitter of greenhouse gases through intensification and land conversion such as deforestation . Last but not least, agriculture dominates pesticide use, which, among others, contaminates surface and ground water and leads to aquatic biodiversity loss . Despite the severity of existing environmental impacts of agriculture, more challenges lie ahead. Global food demand is likely to double in 2050 relative to the 2005 level , driven by population growth and the continuous spread of economic prosperity in developing countries. If the current trend of agricultural practices were to continue, by 2015 about 1 billion hectare of land would be cleared globally, 250 Mt y -1 of nitrogen fertilizers would be used, and 3 Gt y -1 of greenhouse gases would be released . And yet the entrance of agriculture into the energy industry across the world brings more pressure to bear on land, water, and energy that are essential for the production of food for human consumption . In the U.S., for example, corn was primarily used for food and feed before the expansion of the ethanol industry, which now consumes >40% of the total production . As a result, corn area harvested has also expanded substantially , resulting in massive displacement of grassland as well as cropland like cotton . Rapid bio-fuels expansion worldwide, but primarily in the U.S. and EU, has contributed substantially to global food price hikes in the past few years . The increases in food prices have generated dire economic and social consequences worldwide especially for the poor in developing countries. It is against this background that this dissertation investigates three topics related to U.S. agricultural systems. The first chapter explores the environmental implications of land use change from cotton to corn driven partly by ethanol expansion. Previous studies in this area have centered on corn ethanol’s life-cycle GHG emissions , particularly with respect to direct and indirect conversion of natural habitats such as grassland and forest . Insufficient attention has been paid to land use change between crops and associated impacts on the local environment. In the past “ethanol decade,” however, substantial increases in corn prices, due in part to ethanol expansion, not only resulted in considerable conversion of grassland to corn production, but also greatly escalated the dynamics of land use change between crops . There were, for example, land use shifts from soybean, hay, and cotton to corn and from cotton to soybean.

The reason to target cotton to corn, rather than other changes in land use, is as follows. Input requirements for both corn and cotton production are high, thus the environmental implications of land use shift from one to the other are much less clear than from high-input crops to low-input crops or vice versa. The second chapter of the dissertation re-evaluates the calculation of carbon payback time in the case of converting grassland for corn ethanol production. Previous research on the CPT of corn ethanol neglected two important elements that may substantially affect their results, namely, the actual corn yield of the newly converted land and technological advances of the corn ethanol system. The analysis also tests the effect of considering emission timing on the estimates of CPT using dynamic characterization factors as proposed recently in a growing body of literature . The third chapter explores potential changes in the environmental impacts of major crops in the past decade. LCA has been increasingly applied to agricultural systems, as reflected in the number of agricultural LCA databases built in the past few years . As with LCA studies in general, agricultural LCAs often rely on static and single-year inventory data with commonly 5 to 10 years of data age. Literature suggests, however, that agricultural systems may be highly dynamic due to the increasingly changing climate and technological advances such as improved energy efficiency and deployment of genetically modified crops . These factors may bring about substantial changes in the use of input materials and the yield of crops, hence changes in their environmental impacts. Concerns about the negative environmental impacts of fossil fuels, particularly those on climate change and energy security, have driven the recent interest in bio-fuels in the USA . Several federal policies have been put in place to foster bio-fuels development, among which is the ethanol production mandate in the renewable fuel standard . As a result of the favorable policies and gasoline prices, production of corn ethanol in the USA has expanded substantially since 2005, with an annual increase of over six billion liters . Previous research, however, has shown that bio-fuels policies may have caused unintended consequences that not only undermine the goal of the federal policies to reduce greenhouse gas emissions but also degrade local environmental quality . Increasing ethanol demand has contributed to high corn prices, incentivizing farmers to convert grassland into corn growth in the Corn Belt . This direct land use change threatens wildlife habitats and creates a carbon debt that may take up to >100 years to be paid off by replacing gasoline with corn ethanol . Also, due to intensive use of agrochemicals and irrigation water, growing corn on grassland puts further pressure on local water quality and scarcity .

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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 120◦C 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 55◦C 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.