Identifying causes of crop yield variability with interpretive machine learning

Digital soil mapping and assessment for Australia and beyond: A propitious future

(2021) Ross Searle et al.   Geoderma Regional

Uncovering the Past and Future Climate Drivers of Wheat Yield Shocks in Europe With Machine Learning

(2021) Peng Zhu et al.   Earths Future

Soil and terrain properties that predict differences in local ideal seeding rate for soybean

(2020) Emma G. Matcham et al.   AGRONOMY JOURNAL

Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates

(2020) Patrick Filippi et al.   AGRICULTURAL SYSTEMS

Crop yield prediction using machine learning: A systematic literature review

(2020) Thomas van Klompenburg et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning

(2020) Yuri Shendryk et al.   FIELD CROPS RESEARCH

An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning

(2019) Patrick Filippi et al.   PRECISION AGRICULTURE

Mapping the Depth-to-Soil pH Constraint, and the Relationship with Cotton and Grain Yield at the Within-Field Scale

(2019) Patrick Filippi et al.   Agronomy-Basel

Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

(2018) Anna Chlingaryan et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models

(2018) Patrick Filippi et al.   GEODERMA

Towards a national, remote-sensing-based model for predicting field-scale crop yield

(2018) Randall J. Donohue et al.   FIELD CROPS RESEARCH

Causes of wheat yield gaps and opportunities to advance the water-limited yield frontier in Australia

(2018) Zvi Hochman et al.   FIELD CROPS RESEARCH

Quantifying the economic impact of soil constraints on Australian agriculture: A case-study of wheat

(2018) Thomas G. Orton et al.   LAND DEGRADATION & DEVELOPMENT

Machine Learning in Agriculture: A Review

(2018) Konstantinos Liakos et al.   SENSORS

Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains

(2018) Wenxuan Guo   Agronomy-Basel

Digital soil mapping of soil carbon at the farm scale: A spatial downscaling approach in consideration of measured and uncertain data

(2017) Brendan P. Malone et al.   GEODERMA

Google Earth Engine: Planetary-scale geospatial analysis for everyone

(2017) Noel Gorelick et al.   REMOTE SENSING OF ENVIRONMENT

Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements

(2015) Anja Stadler et al.   EUROPEAN JOURNAL OF AGRONOMY

Soil and Landscape Grid of Australia

(2015) M. J. Grundy et al.   Soil Research

Closing Yield Gaps: How Sustainable Can We Be?

(2015) Prajal Pradhan et al.   PLoS One

A Bounds Analysis of World Food Futures: Global Agriculture Through to 2050

(2014) Philip G. Pardey et al.   AUSTRALIAN JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS

Projecting future crop productivity for global economic modeling

(2013) Christoph Müller et al.   AGRICULTURAL ECONOMICS

Yield Trends Are Insufficient to Double Global Crop Production by 2050

(2013) Deepak K. Ray et al.   PLoS One

Global food demand and the sustainable intensification of agriculture

(2011) D. Tilman et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Permutation importance: a corrected feature importance measure

(2010) André Altmann et al.   BIOINFORMATICS

Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network

(2009) Paul H. Hiemstra et al.   COMPUTERS & GEOSCIENCES

Advances in precision agriculture in south-eastern Australia. II. Spatio-temporal prediction of crop yield using terrain derivatives and proximally sensed data

(2009) N. J. Robinson et al.   Crop & Pasture Science

Conditional Variable Importance for Random Forests

(2008) Carolin Strobl et al.   BMC BIOINFORMATICS