Frequent and intense natural disasters like hurricanes and earthquakes in Puerto Rico contribute to unreliable access to food, energy, and water (FEW) resources. Pre-existing political, socioeconomic, and environmental constraints in the archipelago can further complicate access to FEW resources. Bolstering FEW resource security by localizing resource production through household or community-scale projects improves community resilience to natural disasters and strengthens local supply chains. This narrative literature review compares and contrasts projects from around the world designed to strengthen resilience through local resource production or access. Explored solutions address water sources and treatment, land suitability and a variety of food production types, and microgrid solar power systems. The availability of natural resources was determined to make recommendations for how FEW systems could be implemented in Puerto Rican communities seeking to improve their disaster resilience. We seek to provide a framework that allows similar communities outside Puerto Rico to assess potential solutions to improve FEW resource access and understand how to implement them based on their needs and constraints. With systems adaptable to both nominal and disaster resilient conditions, communities can confidently design solutions that create sustainable FEW systems that improve overall FEW resource access.

The communities of Puerto Rico are highly vulnerable to climate change as the archipelago has experienced a multitude of compounding crises and extreme weather events in recent years. To address these issues, the research, analysis, and design of grand challenge solutions for disaster-prone regions like Puerto Rico can utilize collaborative transdisciplinary efforts. Local non-governmental and community-based organizations have a pivotal role in the reconstruction processes and the building of community and environmental resilience in underserved communities. This paper contributes an empirical case study of an online transdisciplinary collaboration between a group of academics and a Puerto Rican non-governmental organization, Caras con Causa. From participant observation, it includes a document analysis of meeting notes with cohort members who were involved in a collaborative National Science Foundation Project, The INFEWS-ER: A Virtual Resource Center Enabling Graduate Innovations at the Nexus of Food, Energy, and Water Systems, with Caras con Causa between October 2020 and April 2021. Caras con Causa focuses on uplifting Puerto Ricans by creating and administering environmental, educational, economic, and community programs, highlighting disaster relief and resilience to help Puerto Rican food, energy, and water systems. Eight key discussion themes emerged from the document analysis: team organization, collaboration with Caras con Causa, deliverables, team contributions, context understanding, participation outcomes, technology setup, and lessons learned. We analyze each of the emerging themes to explain how academics may use transdisciplinary skill sets in addition to standard disciplinary-based approaches or techniques to enhance the institutional capacity of a non-governmental organization doing community resilience work to benefit local food, energy, and water systems. While the learned lessons in this non-governmental organization-academic collaboration may be context-specific, we provide insights that may be generalizable to collaborations in comparable transdisciplinary settings.

There has been an increasing interest in diverting food waste from landfills to enhance renewable resource production due to high greenhouse gas emissions. Sustainable processing of food waste to generate renewable energy represents a significant, though underutilized, opportunity for producing energy. This study proposes an integrated model to simultaneously boost food waste valorization and reduce GHG emissions using geospatial analyses, a clustering algorithm, and a mixed-integer linear optimization program. We maximized net present value by optimizing network and configuration designs in the Illinois food waste system, using stand-alone anaerobic digestion and co-digestion at wastewater treatment plants. We identified the influences of techno-economic factors like prices and logistics parameters on the model objective, electricity production, digestate sales, and system configuration. Results show that installing anaerobic co-digesters at wastewater treatment plants with a total annual capacity of 9.3 million metric tons (Mg) could generate an 8.3% return on investment while reducing CO2 equivalent by approximately one million Mg annually. Tipping price, capital investment, operational cost, and food waste availability were the most significant factors to the net present value of the system. Anaerobic co-digesters were more economically competitive than stand-alone digesters which were only recommended when the tipping price of sludge was lower than food waste. Expanding digestate marketability is crucial to food waste valorization profitability using both stand-alone anaerobic and co-digestion. The minimum tipping prices to operate the anaerobic digestion system were 40 USD/Mg for food waste and 20 USD/Mg for sludge. Depending on the system size and tipping prices, anaerobic digestion and co-digestion could reduce CO2 equivalent between 29 kMg/y and one MMg/y. Expanding the current framework with advanced regional food waste prediction and other bioprocessing techniques will be essential for improving the profitability of food waste valorization and the circular bioeconomy in agriculture.

Puerto Rico has been subject to complex and compounding effects of multiple disasters, exacerbated by sociopolitical, climactic, and geographical challenges that complicate relief and resilience. Interdisciplinary teams are uniquely suited to traverse emerging challenges in post-disaster settings, but there are few studies that leverage transdisciplinary skill sets and virtual co-production of knowledge to build on local autonomous responses. Communities are key sources of information and innovation which can serve as a model for recovery amidst disaster. Thus, an interdisciplinary team of emerging scholars collaborated with Caras con Causa, a local organization in Catan similar to o, Puerto Rico, to develop processes for enhancing autonomous responses to disaster events through participatory pathways, specifically highlighting local knowledge and preferences. The results of this collaboration include: (1) an iterative process model for transdisciplinary co-production in virtual settings and (2) key highlights from post engagement reflections including community-scale definitions of disaster, and limitations to virtual collaboration amidst disaster. Together, these results yielded critical insights and lessons learned, including recommendations for improved project communication methods within transdisciplinary and virtual collaborations. Collectively, the process, it's resulting products, and the post-engagement reflections demonstrate a pathway for scholars and community members to engage disaster resilience challenges. These strategies are most effectively practiced through focused collaboration with community stakeholders and are paramount in solving real-world challenges related to the increasing complex of compounding disasters.

In the context of climate change, crop mapping and yield estimation are critical for improving crop management and ensuring food security. Data-driven methods have succeeded in crop mapping and yield estimation, using a combination of multi-source training data and the application of advanced machine learning technologies. This chapter reviews various data-driven approaches that have been applied in crop mapping and yield estimation, with particular attention given to deep learning algorithms. A typical workflow for data analysis is systematically summarized; it includes multi-source data collection, data preprocessing, model development, model evaluation, and model improvement. The advanced data-driven technologies employed for model development in crop mapping and yield estimation are described and synthesized. This chapter introduces threshold-based methods to illustrate the challenges of intra-class variability and inter-class similarity in crop mapping. Various machine learning and deep learning algorithms are further highlighted in multi-temporal crop mapping analysis, with the consideration of multi-source remote sensing data. For crop yield estimation, typical mechanism models are introduced to clarify the challenges in the context of widely distributed yield heterogeneities across different regions. The data-driven statistical models in yield estimation are thoroughly summarized, with a focus on the utilization of remote sensing and environmental data. Challenges and possible future improvements in data-driven approaches are presented.

Primary production losses often occur due to uncertainties in weather conditions and poor farm management. To opportunely allocate Food, Water and Energy resources, event-based decision-making becomes a key factor in improving system productivity. Key farm events include planting, harvesting, tillage, pesticide and fertilizer applications, cover crop establishment, and biomass harvesting. The objective of event-based decision making is to ensure these operations occur to their maximum effect. Our goal is to explore a successful integration of Food and Energy systems by applying modeling tools to optimize the grain-biomass-fuel supply chain with event-based decision making. By adjusting the window of opportunity for corn harvesting, biomass harvesting becomes more flexible, and both grain and biomass losses are minimized. Preliminary results from a case study in Champaign, Illinois, show that grain system efficiency can be increased as grain losses can be reduced when corn harvesting schedules are optimized. Two key integrations are expected to further increase efficiency and reduce system costs: strategic and tactical decisions being simultaneously considered, as well as fuel processing plants that process both grain and biomass feedstocks. Stakeholders in the farm and industry businesses could greatly benefit from an integrated and more cost-efficient approach for crop residue utilization and renewable energy production.

Nutrient pollution is a major issue in the Mississippi watershed and the Gulf of Mexico. Agriculture in the Midwest is attributed as a major non-point source contributor to this problem. The implementation of best management practices by farmers could greatly reduce the amount of nutrients released from farms into surrounding watersheds. Determining what motivations farmers need in order to implement these practices is a necessary but challenging step in reducing nutrient pollution. A computer model, NitroShed, was created to model the farmer decision-making process and how policy influences could impact adoption rates of best management practices. The model was developed using the agent-based model package Mesa in Python. The model includes a farmer decision-making algorithm to simulate the behavior of farmers considering investments in environmental infrastructure and management practices. The model includes a farmer typology based on the factors farmers consider when they are making decisions. The identified typologies were: Business Oriented, Environmentally Oriented, Innovators, Traditionalists, and Supplementalists. Each group of farmers is unique in the way they consider factors such as risk, social expectations, economics, innovation, and the environment. The model was then run under varying conditions to test adoption rates based on policy changes and financial influences. This will help policy-makers and extension services determine the most effective action plan in increasing farmer adoption of best management practices.

Problems at the nexus of Food, Energy and Water Systems (FEWS) are among the most complex challenges we face. Spanning simple to complex temporal, geographic, social, and political framings, the questions raised at this nexus require multidisciplinary if not transdisciplinary approaches. Answers to these questions must draw from engineering, the physical and biological sciences, and the social sciences. Practical solutions depend upon a wide community of stakeholders, including industry, policymakers, and the general public. Yet there are many obstacles to working in a transdisciplinary environment: unfamiliar concepts, specialized terminology, and countless "blind" spots. Graduate education occurs in disciplinary 'silos', often with little regard for the unintended consequences of our research. Existing pedagogical models do not usually train students to understand neighboring disciplines, thus limiting student learning to narrow areas of expertise, and obstructing their potential for transdisciplinary discourse over their careers. Our goal is a virtual resource center-the INFEWS-ER-that provides educational opportunities to supplement graduate students, especially in their development of transdisciplinary competences. Addressing the grand challenges at the heart of the FEWS nexus will depend upon such competence. Students and scholars from diverse disciplines are working together to develop the INFEWS-ER. To date, we have sponsored both a workshop and a symposium to identify priorities to design the initial curriculum. We have also conducted surveys of the larger community of FEWS researchers. Our work confirms a widespread interest in transdisciplinary training and helps to identify core themes and promising pedagogical approaches. Our curriculum now centers upon several "Cohort Challenges," supported by various "Toolbox Modules" organized around key themes (e.g., communicating science). We plan to initiate the first cohort of students in October of 2018. Students who successfully complete their Cohort Challenges will be certified as the FEW Graduate Scholars. In this paper, we describe the development of this curriculum. We begin with the need for training in transdisciplinary research. We then describe the workshop and symposium, as well as our survey results. We conclude with an outline of the curriculum, including the current Cohort Challenges and Toolbox Modules.

Abstract China has a huge resource potential for biomass‐based renewable energy development, but the resources of field residues are still not effectively used. Rice, maize, and wheat made up 89% of staple crop production in China in 2009. A comprehensive assessment of field residues of these three crops is necessary for the development of biomass‐based industries. This research was based on multiyear county‐level data of crop production, area and yield, as well as the crop phenology information from agrometeorological stations. Spatial and temporal analyses were conducted to quantify the spatial patterns, seasonal variations, and temporal trends of the three major field residues. The mean amount of field residue of rice, maize, and wheat was 470.8 Mt/year from 2002 to 2009. Rice residue topped the field residues at 188.5 Mt/year, followed by maize (152.6 Mt/year) and wheat (129.8 Mt/year). The resource supply of field residues varied temporally throughout the season, where peak months are May, June, September, and October. The resources of all three field residues increased from 2002 to 2009, topped by maize residues at a rate of 10.0 Mt/year. Spatially, high production counties had the fast growth rate and a strong positive spatial autocorrelation. The results showed that the intersection area of East and South Central regions has a spatially concentrated residue density and a stable supply for 5 months. The region can be considered as a suitable region for bioenergy development. A better understanding of spatial and temporal distribution of crop residues could facilitate strategic and tactical bioenergy planning.

Nitrate can be removed from wastewater streams, including subsurface agricultural drainage systems, using woodchip bioreactors to promote microbial denitrification. However, the variations in water flow in these systems could make reliable performance from this microbially-mediated process a challenge. In the current work, the effects of fluctuating water levels on nitrate removal, denitrifying activity, and microbial community composition in laboratory-scale bioreactors were investigated. The performance was sensitive to changing water level. An average of 31% nitrate was removed at high water level and 59% at low water level, despite flow adjustments to maintain a constant theoretical hydraulic retention time. The potential activity, as assessed through denitrifying enzyme assays, averaged 0.0008 mg N2O-N/h/dry g woodchip and did not show statistically significant differences between reactors, sampling depths, or operational conditions. In the denitrifying enzyme assays, nitrate removal consistently exceeded nitrous oxide production. The denitrifying bacterial communities were not significantly different from each other, regardless of water level, meaning that the denitrifying bacterial community did not change in response to disturbance. The overall bacterial communities, however, became more distinct between the two reactors when one reactor was operated with periodic disturbances of changing water height, and showed a stronger effect at the most severely disturbed location. The communities were not distinguishable, though, when comparing the same location under high and low water levels, indicating that the communities in the disturbed reactor were adapted to fluctuating conditions rather than to high or low water level. Overall, these results describe a biological treatment process and microbial community that is resistant to disturbance via water level fluctuations.

In this paper we present a mathematical model for reducing post-harvest loss (PHL) in grain supply chain networks. The proposed model determines the optimal logistics for grain transportation and infrastructure investment by identifying the optimal locations for new pre-processing facilities and by optimising roadway/railway capacity expansion. The objective is to minimise the total system cost, including both infrastructure investment and economic cost from PHL. In this paper we incorporated both quality and quantity PHL during the transportation, transhipment, and pre-processing stages in the supply chain and considers different PHL rates for processed and unprocessed grains. Finally, we conducted a numerical analysis on a real-world network in the State of Illinois and a series of sensitivity analyses to provide insights into the optimal system design under different scenarios. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.

Crop residue grazing is considered one of the simplest and most economical approaches to integrate corn and cattle operations. Allowing cattle to graze crop residues left in the field after harvest can significantly lower feed costs for wintering cows; however, subsequent crop yield might be negatively affected. Developing management strategies to alleviate such impacts requires understanding the spatiotemporal characteristics of cattle location on cropland during grazing. This study has developed an agent-based approach to model cattle movement during crop residue grazing under various management scenarios, based on experimental data during a three-year residue grazing trial in Illinois. A GPS tracking system was implemented to track cattle locations during corn residue grazing under two grazing treatments (continuous grazing vs. strip grazing). The spatial dynamics of cattle movements were explicitly simulated as the results of behavioral decisions made by cattle, based on their internal biological state (e.g. hunger and hydration) and external environment (e.g. heterogeneous forage distribution, supplementation, water, day-night cycles, and management). Simulation results suggested that the model‘s performance was in good agreement with observed GPS data of cattle locations. This model can be used as an aid in future development of decision support for managing integrated crop-livestock systems.

Farm management optimization models have been developed to support decision making for farming activities including planting, harvesting, handling, drying and storage. However, key decisions for maximizing farming output, timing of planting and other culture tasks, are affected by spatiotemporal changes of weather-related and other spatiotemporal, conditions. Using near real-time information describing actual farming decisions related to the timing of farming events across different regions can make the outputs of farm management optimization models more accurate. This study aims to develop analytical tools to extract near real-time information for identifying actual timing of crop planting and harvesting schedules from publicly available databases. Text mining is applied to identify planting and harvesting schedules for different crops in various regions. The spatiotemporal patterns of farming activities are also analyzed by comparing annual farming schedules across different regions. In order to support the reasoning behind scheduling of culture tasks, semantic networks representing relationships between concepts are constructed for gaining a qualitative understanding of factors contributing towards key agricultural decisions. Network analysis and visualization help characterize semantic networks assisting to highlight important information regarding farming activities. Preliminary results show the planting date of corn was severely delayed in 2013 but ahead of average in 2014 in Illinois. These patterns and schedules are in accordance with National Agricultural Statistics Service (NASS) database. Analyses of semantic networks also indicate rain in April was a main factor for planting delays in 2013. Given data describing actual spatiotemporal farming schedules, as well as information of weather-related conditions, we can gain insights determining changes in the timing of farming events that can potentially be improved inputs and thus constraints for farm management models.

Integrated crop and livestock systems have been suggested as a promising means for diversifying agricultural production. In the Midwestern U.S. Corn Belt, tremendous amounts of corn residues can accumulate on the land after harvest. Utilization of residues left in the field as a nutrient source for cattle can be a viable approach to reducing feed cost; however, the effects of directly grazing corn residues on croplands are unclear. Previous studies have shown uneven spatial distributions of cattle locations during grazing, which may potentially lead to spatially heterogeneous impacts on lands. This study seeks to provide a comprehensive understanding of impacts of grazing strategies on spatial patterns of cattle locations during corn residue grazing in central Illinois, and assess potential effects on subsequent crop yield. A GPS tracking system was implemented to track cattle locations during corn residue grazing experiments on an integrated corn-cattle farm from 2012 to 2014. Two grazing management strategies (continuous and strip grazing) were compared to ungrazed areas in a randomized complete block design with three replications. Spatial analysis tools were utilized to quantify land occupancy rates by cattle and identify factors that affect cattle locations. Areas around water tanks and feeders were identified as heavily trafficked regions. Social interactions among cattle were suggested as another factor that affects cattle locations. Preliminary results suggested that no differences in baseline yield were detected before cattle were introduced into the crop system. After fall crop residue grazing experiments were implemented for two years, no significant differences in subsequent crop yield were detected amongst all three treatments. However, grazing may have spatially heterogeneous impacts on subsequent crop yield, as highly trafficked areas were associated with lower corn yield as compared to other regions after the grazing experiment was implemented on the farm. This implies that caution should be exercised to avoid negative impacts when integrating corn and cattle operations through residue grazing. Scientific models and decision support are, therefore, suggested as a viable approach to facilitate the development of best management practices for integrated crop-livestock systems.

Extended Abstract. Agricultural supply chains are complex systems that consist of four major subsystems: production, processing and manufacturing, distribution, and utilization. The agricultural production subsystem is spatio-temporally explicit and is sensitive to changes in climate and weather patterns. It is not only dependent on local-scale environmental factors such as weather and soil properties, but also affected by farm management and infrastructure capabilities. Fluctuations in agricultural productivity impose significant challenges on the design and operations of integrated supply chain systems that couple productivity with farm management decisions within the context of large-scale supply chains. As a result, a key challenge for supply chain modeling is predicting agricultural production across space and time in order to provide better management decisions at each stage – from growth through processing to distribution of goods.

Growing biomass along highways has drawn people’s attention in recent years. After conversion either through combustion to generate heat and electricity, gasification or fermentation to produce gas or liquid fuel, energy products from biomass could be sold to the market, and the revenue may contribute to highway maintenance, or directly fulfill the energy demands of highway maintenance. Pilot studies have been conducted in various states while most of these studies were limited to few selected lands. Although every state provided an estimation of the total biomass potential for its entire state, the spatial distribution of the feedstock remains unclear, which makes it difficult to analyze the real cost or to optimize the feedstock supply chain. The objective of this study is to identify the available lands along Illinois highways and analyze their spatial distribution and relationships with other facilities in the supply chain. To achieve these goals, geospatial information system and remote sensing methods are used to select eligible lands based on the highway database from the Illinois Department of Transportation (IDOT) and aerial images from the USDA. Then, network analysis is performed to address the spatial connections within the land data and with other facilities such as IDOT depots. A generic workflow is developed with the methods employed in this study and can be applied to different states. The results of this study will provide input for system optimization at both county and state levels and therefore offer decision support tools suited to various stakeholders.

To satisfy emerging energy demands, cellulosic biomass shows the potential to improve U.S. energy security and lower greenhouse gases emissions. Cellulosic biomass is valuable and has manifold possible uses, such as manufacturing advanced biofuels, supplying materials for biomass burners and power plants. Growing cellulosic biomass on non-agricultural lands, such as highway right-of-way (ROW), can avoid land use competition between energy and food crops on agricultural lands. Several Departments of Transportation around the U.S. have considered this opportunity because of typically millions of dollars expenditures for mowing and maintaining costs every year. Growing energy crops, while replacing turf grasses, provides opportunity to create economic benefits. This study will analyze the optimal harvesting and logistics operation strategies for highway ROW scenarios for Illinois Department of Transportation (IDOT). Available lands for growing energy crops along highway ROW can be identified and grouped into segments by Geographic Information System (GIS) data analysis. Given the information regarding available lands, biomass feedstock production optimization modeling is applied to determine harvest schedules and estimate biomass yield. Expected results will identify the optimal trip assignment and truck routing strategies for the IDOT scenarios. Estimated operational cost and time are also calculated. These results will provide information for decision makers and equipment operators, not only determining the best operational strategy, but also keeping flexibility for the realistic application.

Metabolism and energetics research have contributed to improving and understanding livestock production efficiency and its associated impacts on the environment. Open-circuit respiration chambers are used to calculate an animal’s heat production and/or emission rate from a combination of respiratory gas exchange, ventilation rate, temperature, and relative humidity measurements. The whole system must be evaluated before application to assess function, integrity, and identify sources of measurement error through a mass recovery (MR) test, which compares the mass measured by the system to a known mass (reference) injected inside the chamber. The objectives of this work were to compare the calculated MR percent and associated standard uncertainty for four reference injection approaches: (1) mass flow control (MFC) of SF6 (tracer gas; TG) and (2) CH4, (3) gravimetric analysis (GA) of SF6, and (4) MFC of SF6 with a diffuser. Three MR tests were conducted in three chambers each, for every approach. Impact of injection location and time to reach the fresh air exchange steady-state was assessed by addition of the diffuser (approach 4) and was found to be ~2 min faster than using a single outlet used in MFC of SF6 and CH4. Significant differences for Chamber 1 (P = 0.002) and Chamber 2 (P = 0.02), but not Chamber 3 were found using a one-way ANOVA comparison, which relies on the variance of the measured MR percent, and does not incorporate measurement error. Further, when standard uncertainty was included, no significant differences between injection approaches were found. This was attributed to the large standard uncertainty associated with the injection approaches, ranging from 3.0% to 14.7%. A well-documented example of performing a MR test and associated uncertainty analysis is needed for understanding the impact of equipment selection (e.g. TG concentration, scale resolution and accuracy) and the number of MR test conducted on whole system verification and uncertainty.

To ensure effective biomass feedstock provision for large-scale biofuel production, an integrated biomass supply chain optimization model was developed to minimize annual biomass-ethanol production costs by optimizing both strategic and tactical planning decisions simultaneously. The mixed integer linear programming model optimizes the activities range from biomass harvesting, packing, in-field transportation, stacking, transportation, preprocessing, and storage, to ethanol production and distribution. The numbers, locations, and capacities of facilities as well as biomass and ethanol distribution patterns are key strategic decisions; while biomass production, delivery, and operating schedules and inventory monitoring are key tactical decisions. The model was implemented to study Miscanthus-ethanol supply chain in Illinois. The base case results showed unit Miscanthus-ethanol production costs were $0.72 L-1 of ethanol. Biorefinery related costs accounts for 62% of the total costs, followed by biomass procurement costs. Sensitivity analysis showed that a 50% reduction in biomass yield would increase unit production costs by 11%. (C) 2014 Elsevier Ltd. All rights reserved.