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Palosuo, T. (2013). Data format for model in- and output (Vol. 2).
Abstract: A common format for model input variables and model output variables has been defined to be distributed to modellers participating in the model inter-comparison and improvement. The aim of common formats is to support the communication between the modellers, those providing empirical data of the experiments and those analysing the simulation results. The input format facilitates the model application in a way that each cropping-system to be modelled will be defined in the same way. Data will be delivered in EXCEL sheets with sub-tables for each block of inputs. Tables are mostly organized in a way that allows export and sequential read-in by the models. The common output format enables effective processing of results estimating model performance indicators. No Label
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Bellocchi, G., Ma, S., Köchy, M., & Braunmiller, K. (2013). Datasets classification and criteria for data requirements (Vol. 2).
Abstract: This deliverable focuses on the collation, screening, and consolidation of data for selected grassland sites in Europe and peri-Mediterranean regions. No Label
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Trnka, M., Kersebaum, K., Christian,, & Olesen, J. E. (2015). Description of the compiled experimental data available in the MACSUR CropM database (Vol. 6).
Abstract: The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) have been collected through out the project together with data for additional analysis of abiotic factors influencing yields. A list of possible dataset was collated in the first year of project however very few of the existing datasets were found usable for the crop model simulation as they fell short of the requirements defined in the part 2.3. However database has been populated as planned with the results of the ongoing MACSUR studies and will serve in the same way for the MACSUR 2 duration. No Label
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Persson, T. (2015). Determining the variability in optimal sowing date of spring cereals in South Eastern Norway (Vol. 5).
Abstract: Spring cereals are important agricultural crops in Northern Europe. The short growing season in this region necessitates early sowing. The earliest possible date is often determined by the soil water content, which usually decreases during and after snowmelt at rates varying with the weather and the soil characteristics. Tillage and sowing operations on soils with too high a water content can lead to soil compaction, increased soil erosion, and losses of nutrients and soil organic matter. Rainfall intensity also affects crop emergence, through its potentially negative effects on surface capping. The objective of this study was to determine the earliest possible sowing date of spring cereals for representative soil and climate scenarios in southeastern Norway. Criteria were set for pre-sowing tillage operations and sowing, based on the water content in differ soil layers and the incidence of rainfall. To determine the day of the year when these criteria were first met, the soil water content during the spring was simulated with the soil module in DSSAT v4.5. These simulations were performed for contrasting soil types and climate scenarios representing the period 1961-90 and 2046-65 respectively. For each combination of soil and climate, one hundred simulations with individual weather data were performed. The results provide information about the timing and variability of the optimal planting date for the current and projected climate in South Eastern Norway. No Label
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Wallach, D., & Rivington, M. (2013). Development of a common set of methods and protocols for assessing and communicating uncertainties (Vol. 2).
Abstract: This reports sets out an outline approach to create definitions of uncertainty and how it might be classified. This is not a prescriptive approach rather it should be seen as a starting point from which further development can be made by consensus with CropM partners and across MACSUR Themes. We propose both a numerical quantification of uncertainty and text based classification scheme. The rational is to be able to both establish the terms and definitions in quantifying the impact of uncertainty on model estimates and have a scheme to enable identification of connectivity between types and sources of uncertainty. The aim is to establish a common set of terms and structure within which they operate that can be used to guide work within CropM. No Label
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