Z. Hochman

Why is there so much variability in crop multi-model studies?

The paper investigates variability in crop model studies and finds parameter uncertainty contributes more than twice as much as structural uncertainty to total variability.

Proposal and extensive test of a calibration protocol for crop phenology models

This paper proposes and tests a calibration protocol for crop phenology models, reducing prediction error and team variability by 22%.

The chaos in calibrating crop models

The paper proposes a method to develop calibration guidelines for system models by analyzing multi-model simulations, applying it to crop model phenology calibration and providing recommendations on objective functions, parameters, and software.

Multi-model evaluation of phenology prediction for wheat in Australia

This study evaluates wheat phenology prediction models in Australia, finding that multi-model averaging performs nearly as well as the best single model, with an average error of 9 days.

How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

This study evaluates how well 27 crop modeling groups predict wheat phenology in Northern France using calibration data from the same target population, finding mean absolute errors comparable to measurement error.