Can we used remote sensing to calibrate crop models?
Uncertainty decomposition of maize yield in Spain.
Lucerne Yield Gaps in Argentina.
Crop Model Uncertainty Decomposition.
Main drivers of sorghum biomass in the USA.
Drivers and constraints of on‑farm diversity.
We assessed the errors during simulation configuration in APSIM-Potato using GxExM experiments worldwide.
We determine and quantify the main drivers of potato yield variability under irrigated conditions.
We evaluated the difference of yield and irrigation using input/output aggregation.
We model phenological variation with sowing date and cultivar for lentil and faba bean against the climatic patterns of frost and heat.
We quantify the data aggregation effects on potato yield and irrigation requirements.
We compared two forage landcovers, the sequence oats-maize and pure alfalfa across a mean annual precipitation gradient.
We compared resource use efficiency of forage crop sequences and temperate perennial pastures.
We assessed the association between root biomass, C-OM, C in mineral-associated organic matter (C-MAOM), and C in particulate organic matter (C-POM) and its vertical distribution.
We assessed the accuracy of APSIM Classic to simulated corn yield and subsurface artificial drainage in north central Indiana US.
We assessed the accuracy of APSIM Classic to simulated the yield of forage crop sequences in the Argentinian Pampas.
We evaluated the ability of APSIM to predict the dry matter yield of switchgrass and Miscanthus at several US locations.
We evaluated the capacity of APSIM to simulate the growth rates and predict the dry matter yield of Lucerne (Medicago sativa L.) and annual ryegrass (Lolium multiflorum Lam.) in contrasting climatic regions of Argentina and Australia.