Isaiah Huber

current project: modeling days suitable for field work using a variety of process-based and statistical models. 

Shahhosseini M, Hu G, Huber I, Archontoulis S, 2021. Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt. Nature Scientific Reports, 11:1606.

Baum M, Licht M, Huber I, Archontoulis SV, 2020. Impacts of climate change on the optimum planting date of different maize cultivars in the central US Corn Belt. European J Agronomy 119, 126101.

Pasley HR, Huber I, Castellano MJ, Archontoulis SV, 2020. Modeling flood-induced stress in soybeans. Frontiers Plant Science 11:62.

Archontoulis SV, Castellano MJ, Licht MA, Nichols V, Baum M, Huber I, Martinez-Feria R, Puntel L, Ordónez RA, Iqbal J, Wright EE, Dietzel RN, Helmers M, Vanloocke A, Liebman M, Hatfield JL, Herzmann D, Cordova SC, Edmonds P, Togliatti K, Kessler A, Danalatos G, Pasley H, Pederson C, Lamkey KR, 2020. Predicting Crop Yields and Soil-Plant Nitrogen Dynamics in the US Corn Belt. Crop Science, 60: 721–738.

Bartel CA, Archontoulis SV, Lenssen AW, Moore KJ, Huber I, Laird DA, Dixon PW, 2020. Modeling perennial groundcover effects on annual maize grain crop growth with APSIM. Agronomy J, 112, 1895–1910

Ebrahimi-Mollabashi E, Huth NI, Holzwoth DP, Ordonez RS, Hatfield JL, Huber I, Castellano MJ, Archontoulis SV, 2019. Enhancing APSIM to simulate excessive moisture effects on root growth. Field Crops Research 236: 58–67.

Martinez-Feria R; Castellano M; Dietzel R; Helmers M; Liebman M; Huber I; Archontoulis SV, 2018. Linking crop- and soil-based approaches to evaluate system nitrogen-use efficiency and tradeoffs. Agriculture, Ecosystems and Environment 256: 131–143

Archontoulis SV, Huber I, Miguez FE, Thorburn PJ, Rogosvka N, Laird DA, 2016. A model for mechanistic and system assessments of biochar effects in soils and crops and trade-offs. GCB-Bioenergy 8, 1028–1045