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This body of work illustrates that omitted agricultural processes may be a potential source of model land surface biases. However, their inclusion can better inform and improve modern day ESM climate simulations, evidenced by the studies reviewed herein. Such datasets can currently provide only a modern average or, in some cases, a “snapshot”, of global agricultural extent and management. Some datasets for irrigation, on the other hand, merge global irrigated areas data with model estimates that can be extrapolated to time-varying irrigation rates. Many of these datasets, such as crop coverage and planting dates, are specific to present-day distributions of agriculture. With these caveats in mind, several datasets and techniques are available and/or under development that provide high resolution (ranging from 5 minute - 0.5 degree) modern global average crop coverage, planting and harvest dates, irrigation, and nitrogen applications, and ranges of interannual variability. As such, while there is still utility in leveraging new and evolving datasets that speak to global and regional agricultural coverage and management, there is also a need to to be wary and cognizant of their deficiencies and potential uncertainties. This leads to significant differences between modeled and data-based estimates of relevant climate variables, agricultural water use, and production, particularly when local crop coverage and management conditions are highly dependent on climate conditions. Even in countries with relatively advanced data collection, survey methods, and large areas of high productivity, inconsistencies in reported statistics and other datasets make model validation and process evaluation challenging. These data are generally spatially aggregated and thus eliminate needed information on spatial heterogeneity. Many of those available – such as through the UN Food and Agriculture Organizations Statistics Database – rely on government reported production and management information. However, there has been a relative paucity of reliable production and management datasets for ESM development. Such datasets are vital to improved ESM representations of agriculture and their validation of simulated agro-climatic interactions. These must include spatial and temporal information on crop coverage, phenology, and management conditions.
#Simulating tied ridges apsim full
To realize the full utility of these model intercomparisons, and contextualize their findings and implications, more comprehensive agricultural datasets are required.
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Associated Data Data Availability Statement