  A core task in precision agriculture is the identification of climatic and
ecological conditions that are advantageous for a given crop. The most succinct
approach is geolocation, which is concerned with locating the native region of
a given sample based on its genetic makeup. Here, we investigate genomic
geolocation of Populus trichocarpa, or poplar, which has been identified by the
US Department of Energy as a fast-rotation biofuel crop to be harvested
nationwide. In particular, we approach geolocation from a reference-free
perspective, circumventing the need for compute-intensive processes such as
variant calling and alignment. Our model, MashNet, predicts latitude and
longitude for poplar trees from randomly-sampled, unaligned sequence fragments.
We show that our model performs comparably to Locator, a state-of-the-art
method based on aligned whole-genome sequence data. MashNet achieves an error
of 34.0 km^2 compared to Locator's 22.1 km^2. MashNet allows growers to quickly
and efficiently identify natural varieties that will be most productive in
their growth environment based on genotype. This paper explores geolocation for
precision agriculture while providing a framework and data source for further
development by the machine learning community.
