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Process-based agroecosystem models are powerful tools to assess performance of managed landscapes, but their ability to accurately represent reality is limited by the types of input data they can use. Ensuring these models can represent cropping field heterogeneity and environmental impact is important, especially given the growing interest in using agroecosystem models to quantify ecosystem services from best management practices and land use change. We posited that augmenting process-based agroecosystem models with additional field-specific information such as topography, hydrologic processes, or independent indicators of yield could help limit simulation artifacts that obscure mechanisms driving observed variations. To test this, we augmented the agroecosystem model Agricultural Production Systems Simulator (APSIM) with field-specific topography and satellite imagery in a simulation framework we call Foresite. We used Foresite to optimize APSIM yield predictions to match those created from a machine learning model built on remotely sensed indicators of hydrology and plant productivity. Using these improved subfield yield predictions to guide APSIM optimization, total N O 3 − N loss estimates increased by 39% in maize and 20% in soybeans when summed across all years. In addition, we found a disproportionate total amount of leaching in the lowest yielding field areas vs the highest yielding areas in maize (42% vs 15%) and a similar effect in soybeans (31% vs 20%). Overall, we found that augmenting process-based models with now-common subfield remotely sensed data significantly increased values of predicted nutrient loss from fields, indicating opportunities to improve field-scale agroecosystem simulations, particularly if used to calculate nutrient credits in ecosystem service markets.

Wicked problems are inherent in food–energy–water systems (FEWS) due to the complexity and interconnectedness of these systems, and addressing these challenges necessitates the involvement of the diverse stakeholders in FEWS. However, successful stakeholder engagement requires a strong understanding of the relationships between stakeholders and the specific wicked problem. To better account for these relationships, we adapted a means, motive, and opportunity (MMO) framework to develop a method of stakeholder analysis that evaluates the agency of stakeholders related to a wicked problem in FEWS. This method involves two key components: (1) identification of a challenge at the FEWS nexus and (2) evaluation of stakeholder agency related to the challenge using the dimensions of MMO. This approach provides a method for understanding the characteristics of stakeholders in FEWS and provides information that could be used to inform stakeholder engagement in efforts to address wicked problems at the FEWS nexus. In this article, we present the stakeholder analysis method and describe an example application of the MMO method by examining stakeholder agency related to the adoption of improved swine waste management technology in North Carolina, USA.

Agricultural landscapes like the US Corn Belt are abundant producers of grains and animal co-products. However, these high-input, monocropped fields are not resilient to environmental or market fluctuations. Diversifying or changing their management would provide both economic and ecosystem service benefits to the agricultural landscape and the general public. Advances in machine learning and digital agriculture (DA) have allowed for unprecedented analysis of fields at both the micro and macro levels. These advances could help farmers, farm managers, and other agricultural stakeholders when it comes to maximizing productivity but also creating more resilient economic and environmental systems. This dissertation explores three main research areas with the goal of creating a more resilient modern row-crop agriculture and designing DA and decision-support systems (DSS) to be useful for agricultural stakeholders. The first research area explores how to analyze landscapes for productivity and ecosystem services using a combination of remote sensing, monitoring, machine learning (ML), and a process-based agroecosystem model for latent environmental variables. The second area explores the process of implementing diversification and conservation practices on the agricultural landscape by interviewing farmers who had already taken steps to diversify their farming operations. The third area considers the socio-economic factors that currently impact the agricultural input and machinery industries; how these factors could also affect the DA industry if left unaddressed; and novel ways in which DA could be applied to analyze and manage agricultural fields more holistically.In Chapter 2, we explored how scientific process-based agroecosystem models can powerful tools to assess performance of managed agroecosystems. However, their ability to accurately represent reality is limited by the types of input data they can use. Ensuring process-based agroecosystems models can simulate field heterogeneity and estimate environmental impact is important for agroecosystem performance assessments, especially given the growing interest in using agroecosystem models for ecosystem service markets. Similarly, ML is being used to predict agroecosystem variables and productivity. Machine learning is well-suited to making heterogenous predictions, but lacks scientific underpinnings and often transparency (i.e., black boxes). We posited that augmenting process-based agroecosystem models with additional field-specific information such as topography, hydrologic processes, or independent indicators of yield could help limit simulation artifacts that obscure model representation of the landscape. To test this hypothesis, we augmented the agroecosystem model APSIM with remote sensing data and ML in a simulation framework we call Foresite. We used Foresite to parameterize APSIM yield predictions to match those created from a ML model built on remotely sensed indicators of hydrology and plant productivity. By parameterizing APSIM to ML yield predictions, total NO3-N loss estimates increased by 39% in maize and 20% in soybeans. We also found a disproportionate total amount of leaching in the lowest yielding field areas vs the highest yielding areas in maize (42% vs 15%) and a similar effect in soybeans (31% vs 20%). Augmenting process-based agroecosystem models with remote sensing and ML dramatically changed simulated nutrient loss values, both within and across fields. These findings indicate opportunities to improve both field and landscape scale agroecosystem simulations, and opportunities for land use or management changes in the most environmentally sensitive field areas.In Chapter 3, we looked at how decision support systems (DSS) could be designed to assist farmers in making land-use diversification decisions. Currently, adoption of DSS by farmers remains low, likely due to lack of farmer engagement before and during the development process. This study aimed to better understand the tasks, tools, and people involved in implementing farmland diversification. The findings were then translated as recommendations for future DSS. Semi-structured interviews were conducted with 11 farmers who had diversified their farming operation in the past four years. Interview data was transcribed and then analyzed using affinity diagramming, a method well-suited to analyzing large amounts of qualitative data. We identified ways in which DSS could assist with diversification decision-making as well as structural barriers that could likely not be overcome by DSS. We found that participants layered multiple sources of data and observations over several years to identify field productivity trends and drivers. Participants used spatial orientation of practices to fit management and logistical considerations. Financial planning and market identification was also important when adopting new practices. In addition, DSS can work to fill end-user knowledge gaps such as optimal management for pollinator habitat. However, despite these findings, additional barriers to diversification were identified that DSS cannot or can only partially address. These include social pressures in the farming community, lack of markets, crop insurance policies, and having positive or negative relationships with governmental organizations. It is likely that multiple interventions will be needed to successfully diversify the agricultural landscape and support economic and ecosystem health.For Chapter 4, I have written an opinion piece on use of digital agriculture (DA), considering the socio-economic factors that currently impact the agricultural industry; how these factors could affect the use of DA if left unaddressed; and novel ways in which DA could be applied to analyze and manage agricultural fields more holistically. Digital agriculture or ‘Agriculture 4.0’ has received increased attention as a promising pathway to more sustainable agricultural production. However, adoption of prior agricultural technologies has not always benefitted farmers or the public in the long term. Recent literature has explored how DA can be used for agronomic and environmental benefit as well as the ethical and social implications of these new technologies. This chapter expands on these recent works to examine the current state of DA and how it is currently being applied on farms and in research. I also look at how past technological advances have both benefitted and failed farmers and citizens, including increased and disproportionate market concentration of inputs and machinery over the past several decades. I then describe how integrating multiple DA data sources over multiple years of record can be used to analyze fields and cropping systems at a more holistic level. Lastly, I look at novel ways in which DA can be applied beyond the current, predominant state of input-based and productivity-focused DA. I argue that if DA is to become beneficial as a whole and to a wide range of persons we will need to 1) find and bring value in DA to multiple stakeholders, including small-scale farmers, citizens, and marginalized demographics; 2) promote open-source practices and standards to encourage innovation and development; and 3) harness big data and DA to foster environmental and indirect benefits on the agricultural landscape in addition to agronomic production.The culmination of these three research chapters is ultimately a more modern, diverse, and resilient agriculture. Digital agriculture technologies must incorporate the agronomic, social, and environmental if they are meant to foster new ways to manage agricultural landscapes and maximum agronomic and social ROI. It will take more interdisciplinary research efforts to achieve this goal, including the physical and social sciences together with citizen engagement. If not, it is likely that DA will continue the current trajectory of agriculture, focusing on short-term solutions and not the root cause of agricultural problems.

Diversifying high-input, monocropped landscapes like the US Corn Belt would provide both economic and ecosystem service benefits to the agricultural landscape. Decision support systems (DSS) and digital agriculture could help farmers decide if diversification is suitable for their operation. However, adoption of DSS by farmers remains low, likely due to lack of farmer engagement before and during the DSS development process. This study aimed to better understand the tasks, tools, and people involved in implementing farmland diversification with the goal to inform design of agricultural DSS. Semi-structured interviews were conducted with 11 farmers who had diversified their corn/soybean cropland with government-supported conservation programs (e.g., CRP, wetlands) and alternative crops (e.g., small grains, pasture) in the past four years. Interview data was transcribed and then analyzed using affinity diagramming. Results show farmers needed DSS to layer multiple sources of data and observations over several years to identify field productivity trends and drivers; spatial orientation of practices to fit management and field constraints; matching operation goals to alternative practices; financial planning and market exploration; and information on promising emerging practices like subsidized pollinator habitat. However, the interviews also highlighted structural barriers to diversification that DSS cannot or can only partially address. These included social pressures; market access; crop insurance policy; and quality of relationships with governmental agencies. Results indicate better DSS design can empower individual farmers to diversify cropland, but structural interventions will be needed to successfully diversify the agricultural landscape and support economic and ecosystem health.

Wicked problems are inherent in food–energy–water systems (FEWS) due to the complexity and interconnectedness of these systems, and addressing these challenges necessitates the involvement of the diverse stakeholders in FEWS. However, successful stakeholder engagement requires a strong understanding of the relationships between stakeholders and the specific wicked problem. To better account for these relationships, we adapted a means, motive, and opportunity (MMO) framework to develop a method of stakeholder analysis that evaluates the agency of stakeholders related to a wicked problem in FEWS. This method involves two key components: (1) identification of a challenge at the FEWS nexus and (2) evaluation of stakeholder agency related to the challenge using the dimensions of MMO. This approach provides a method for understanding the characteristics of stakeholders in FEWS and provides information that could be used to inform stakeholder engagement in efforts to address wicked problems at the FEWS nexus. In this article, we present the stakeholder analysis method and describe an example application of the MMO method by examining stakeholder agency related to the adoption of improved swine waste management technology in North Carolina, USA.