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Xing Liu, Jeff Andresen, Haishun Yang, and Dev Niyogi

Abstract

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, and Mead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days’ accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre−1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing crop-specific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies.

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Rezaul Mahmood, Ryan Boyles, Kevin Brinson, Christopher Fiebrich, Stuart Foster, Ken Hubbard, David Robinson, Jeff Andresen, and Dan Leathers

Abstract

Mesoscale in situ meteorological observations are essential for better understanding and forecasting the weather and climate and to aid in decision-making by a myriad of stakeholder communities. They include, for example, state environmental and emergency management agencies, the commercial sector, media, agriculture, and the general public. Over the last three decades, a number of mesoscale weather and climate observation networks have become operational. These networks are known as mesonets. Most are operated by universities and receive different levels of funding. It is important to communicate the current status and critical roles the mesonets play.

Most mesonets collect standard meteorological data and in many cases ancillary near-surface data within both soil and water bodies. Observations are made by a relatively spatially dense array of stations, mostly at subhourly time scales. Data are relayed via various means of communication to mesonet offices, with derived products typically distributed in tabular, graph, and map formats in near–real time via the World Wide Web. Observed data and detailed metadata are also carefully archived.

To ensure the highest-quality data, mesonets conduct regular testing and calibration of instruments and field technicians make site visits based on “maintenance tickets” and prescheduled frequencies. Most mesonets have developed close partnerships with a variety of local, state, and federal-level entities. The overall goal is to continue to maintain these networks for high-quality meteorological and climatological data collection, distribution, and decision-support tool development for the public good, education, and research.

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