Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California's Almond Orchards.

ABSTRACT: Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological d

SUBMITTER: Jin Y 

PROVIDER: S-EPMC7082403 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature