State spaces for agriculture: A meta-systematic design automation framework

Runck, Bryan C.; Streed, Adam; Wang, Diane; Ewing, Patrick M.; Kantar, Michael B.; Raghavan, Barath

doi:10.1093/pnasnexus/pgad084

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…High-value and nonflammable crops can improve ecological benefits while mitigating wildfire hazards. Complex agroecosystems are becoming more prevalent in land use planning to ensure ecological benefits while considering bio-diversity ( 94 ). Banana buffers are a step towards multifunctional landscapes.…”

Section: Discussionmentioning

confidence: 99%

Edible fire buffers: Mitigation of wildfire with multifunctional landscapes

Fu,

Lidar,

Kantar

et al. 2023

PNAS Nexus

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Wildfires ravage lands in seasonally dry regions, imposing high costs on infrastructure maintenance and human habitation at the wildland–urban interface. Current fire mitigation approaches present upfront costs with uncertain long-term payoffs. We show that a new landscape intervention on human-managed wildlands—buffers of a low-flammability crop species such as banana irrigated using recycled water—can mitigate wildfires and produce food profitably. This new intervention can complement existing fire mitigation approaches. Recreating a recent, major fire in simulation, we find that a medium-sized (633 m) banana buffer decreases fireline intensity by 96%, similar to the combination of prescribed burns and mechanical thinning, and delays the fire by 316 min, enabling safer and more effective firefighting. We find that under climate change, despite worsened fires, banana buffers will still have a protective effect. We also find that banana buffers with average yield could produce a profit of $56k USD/hectare through fruit sales, in addition to fire mitigation.

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Section: Discussionmentioning

confidence: 99%

Edible fire buffers: Mitigation of wildfire with multifunctional landscapes

Fu,

Lidar,

Kantar

et al. 2023

PNAS Nexus

Self Cite

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A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

Gall,

Glancy,

Kantar

et al. 2024

Agricultural & Env Letters

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Agrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim‐Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser‐based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi‐source agrometeorology data.

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Simulated exploration of parameter space and resource allocation for strategic creation of neo‐domestication breeding programs

Fumia,

Zenil‐Ferguson,

Wolfe

et al. 2024

Crop Science

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Modern domestication efforts have occurred in a wide range of species. These efforts have led to different levels of change from the initial wild populations and market success. In this study, we explore different breeding cycle parameters to understand the rate of change in different potential starting points of neo‐domestication breeding programs. The design of a program that will bring a new crop to market requires consideration of cost efficiency and resource allocation. More crop diversity on the market creates an opportunity to design different types of food systems that can be tailored toward regional and local food security. Stochastic simulation of the rate of gain and variance is applied to different parameter combinations through the breeding cycle—crossing, evaluation, and selection—to identify population level changes along the continuum of wild to semi‐domestic plant species. The simulated breeding schemes differ in phenotypic gain and variance depending on selection strategy and population type, discovering the largest phenotypic gain of oligogenic traits occurring using phenotypic recurrent selection for landrace and orphan populations while choosing genomic selection for wild populations. There were also differences based on selection strategy, with maximum avoidance consistently leading to lower gains but higher additive variance. Overall, when looking to domesticate a new species, our simulations find phenotypic recurrent selection to be the most cost‐effective option and lead to the most gain in early generations of selection, with marker technology being most useful once initial gains have plateaued.

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State spaces for agriculture: A meta-systematic design automation framework

Cited by 4 publications

References 31 publications

Edible fire buffers: Mitigation of wildfire with multifunctional landscapes

Edible fire buffers: Mitigation of wildfire with multifunctional landscapes

A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

Simulated exploration of parameter space and resource allocation for strategic creation of neo‐domestication breeding programs

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