Agriculture is a critical component of the human food system. Agricultural systems are prone to change over time due to rising environmental issues, advances in agricultural technology, changing needs of an ever-growing human society, and a dynamic economic context. The coupling of agriculture to the success of human societies and the impact of agricultural practices on the environment is nontrivial. Varied efforts---including new regulations, certifications, techniques and software---exist to assess and improve the sustainability of agriculture. A wide variety of environmental assessments are conducted, standards written to govern production, algorithms designed to optimize supply chains, and food labels created to assist in consumer decision-making. However, it is a time-consuming and expensive venture to create formal models for such analyses using current methods and software. Multiple stakeholders in a fragmented field, with tensions and pulls in different directions, results in a duplication of efforts and disconnected data and processes.
To explore the challenges that exist in modeling sustainable agriculture, I characterize environmental assessment as a modeling process, and secondly, characterize sustainable agricultural systems as a type of complex adaptive system. Framing the assessment process and system of interest in this manner permits the application of various techniques from software engineering, systems analysis, and human-computer interaction to tease apart the core issues and to subsequently respond to these challenges through design.
First, I present an analysis of the capacity of Life Cycle Assessment (a formal and quantitative environmental assessment technique) to represent small- to medium-scale sustainability-oriented farms. Then, I described a qualitative field study, in which I visited 16 farms across California, interviewing sustainability-oriented farmers, and collecting samples of farm data. The goal of this study was to uncover how and why farmers model farms in practice, the nature and availability of farm data, and the experiences of farmers with various environmental assessment techniques.The findings of these two studies resulted in the articulation of domain-specific modeling requirements. These include: creating selective and partial system models, knitting together qualitative and quantitative data in system models, capturing both spatial and temporal structures, and all of this through models that are abstract yet grounded in real farm data.
Building on these studies, I present MoSS: a framework to enable the Modeling of Sustainable Systems. MoSS consists of three parts: an abstract model, domain-specific elements to allow for modeling agricultural systems, and model 'perspectives' that allow for the assessment of the environmental performance of the system. I conducted a scenario-based evaluation of MoSS to assess its ability to express the varying dynamism and complexity of sustainable agricultural systems. MoSS addresses the core challenges involved in modeling sustainable agriculture, providing a consistent mechanism to capture the essence of farms. MoSS represents a step forward in grounded information design for sustainable agriculture, paving the way for the design of information management and environmental assessment tools that more closely meet the needs of small- to medium-scale farms and farmers. Through the work presented in my dissertation, I have also demonstrated how one may engage in applied and interdisciplinary software engineering research to support sustainable development.
Sustainability is not supported by traditional software engineering methods. This lack of support leads to inefficient efforts to address sustainability or complete omission of this important concept. The aim of the SE4S project is to support the dimensions of sustainability - human, social, economic, environmental, and technical - within different phases of the software lifecycle, with a focus on requirements engineering (RE) and quality assurance (QA). For more, click here.
Work conducted with the California Plug Load Research Center, specifically looking into how people use power management on personal computers. This project focused on human behaviour and implications for power management settings that are decided on the policy and manufacturing end.
Research was conducted over one year as part of my Master's thesis. I aimed to develop a tool and method through which developers could gain access to energy data, enabling them to identify what major the energy sinks in their software development environment were. This work is now being ported into a general energy monitoring tool.