Advanced Analytics for Predicting Spatiotemporal Agricultural Outcomes

Ippolito, Tara A.

Graduate Thesis Or Dissertation

Advanced Analytics for Predicting Spatiotemporal Agricultural Outcomes Public Deposited

Analytics

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/qv33rz34k

Abstract

Agriculture supports economies, food security, and the livelihoods of diverse communities around the world. As a key component of many of the United Nations sustainable development goals, ensuring the success of agriculture and reducing its impact on the environment are critical to global progress. While increasing global agricultural production is necessary to meet rising population and lifestyle changes, many modern management practices have contributed to degradation, declines in soil fertility, losses of natural habitat, and increases in global greenhouse gas emissions. Improving productivity and reducing the environmental impact of agricultural systems are challenging to balance, particularly as climate change threatens global agriculture and con- strains adaptive capacity. Balancing productivity and sustainability is also dependent on the scale- dependent spatiotemporal heterogeneity of agroecosystems which drives agricultural outcomes. In this dissertation, I investigate sustainability and productivity outcomes of diverse agricultural systems and explore the ways that biophysical conditions stratify outcomes across landscapes. Using a wide variety of remotely-sensed, modeled, machine-learning predicted and field-measured data, I use advanced analytical approaches to predict agricultural outcomes as a result of unique soil, climate, and plant characteristics. To investigate sustainability outcomes, I predict changes in soil organic carbon across Europe as a result of differing management practices and explore how soil and climate conditions determine these responses. In Kenya, I predict the productivity outcomes of rangeland vegetation in order to spatiotemporally characterize the landscape’s forage resources for livestock grazing and identify drivers of forage availability during periods of low productivity. Finally, in order to simultaneously investigate sustainability and productivity outcomes, I build Land Capability Classifications for the Dosso region of Niger in order to inform land-use planning efforts and identify areas which are the most suitable for sustainable agricultural uses. From these analyses, I identify management opportunities and limitations with respect to a variety of goals such as optimizing land use, improving soil carbon storage, and reduction of drought-related losses. The frameworks and methodologies developed in this dissertation can be readily applied in any location, at any scale, to develop actionable insights for agricultural systems.

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