![]() Why our work is important: We fill the need for an organization in southwest Ohio that is dedicated to land conservation. CLC (1) accepts donations of conservation easements from landowners, (2) accepts gifts of land and (3) purchases land. What we do: To preserve the natural lands and farmlands of southwest Ohio. CLC uses LTA’s "Land Trust Standards and Practices" to guide our work as we grow. ![]() CLC is a member of the national Land Trust Alliance (LTA), and Coalition of Ohio Land Trusts (COLT). More about CLC: We are a private, nonprofit, tax-exempt conservation organization, incorporated in September 1999 under the laws of the state of Ohio. Other conservation options offered include Land Donation, and Bargain/Conservation Sale of Land. Using a variety of land protection tools - including the Conservation Easement - land trusts work closely with landowners who wish to legally preserve their lands. that work to preserve land for its natural, recreational, scenic, historic or agricultural value. What is a Land Trust? Our organization is known as a land trust, a historical term used to describe more than 1200 organizations in the U.S. This model of expansion and merger of land trusts is successful in northeast Ohio, southern Indiana and throughout the country. ![]() The merger integrates our respective assets, expertise, connections and good will for greater reach and effectiveness. With implications for manual and automatic data streams and model updating, our study highlights that the success of Bayesian methods for predictions depends on a comprehensive understanding of the inherent structure in the observation data and of the model limitations.In 2015, after working together for more than a year, three land trusts merged to become one: Cardinal Land Conservancy-one strong regional land trust that works in seven southwest Ohio counties to preserve quality of life in the places you love. This could be explained by differences in ripening group and temperature conditions during vegetative growth. However, in the true sequences that followed the actual chronological order of cultivation by the farmers in the two regions, prediction error increased when the calibration data were not representative of the validation data. For two sequences using synthetic data, one in which the model was able to accurately simulate the observations, and the other in which a single cultivar was grown under the same environmental conditions, prediction error was mostly reduced. Parameter uncertainty was reduced as expected with the sequential updates. Parameter uncertainty and model prediction errors were expected to progressively be reduced to a final, irreducible value. ![]() We used field measurements of silage maize grown between 20 in the regions of Kraichgau and the Swabian Alb in southwestern Germany. In this study, we used a Bayesian sequential updating (BSU) approach to progressively incorporate additional data at a yearly time-step in order to calibrate a phenology model (SPASS) while analysing changes in parameter uncertainty and prediction quality. Bayesian calibration allows for the estimation of model parameters and quantification of uncertainties, with the consideration of prior information. However, robust predictions are hampered by uncertainty in crop model parameters and in the data used for calibration. Crop models are tools used for predicting year-to-year crop development on field to regional scales. ![]()
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