Abstract
Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2RASC) and a regression analysis (RAASC) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007–2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data.
Highlights
The behavior of crops under environmental conditions and cultivation practices can be analyzed with the useful tool and technique of crop growth models
Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data
The highest variance was obtained for the driest area with light-textured soils; two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and two soil moisture-based products show very good results in the more humid areas
Summary
The behavior of crops under environmental conditions and cultivation practices can be analyzed with the useful tool and technique of crop growth models. Consisting of one or more mathematical equations, descriptive or empirical models define the behavior of a system or part of a system in a simple manner [1], such as agrometeorological indices These can be an efficient tool to relate various crop responses to environmental observations if the Atmosphere 2018, 9, 290; doi:10.3390/atmos9080290 www.mdpi.com/journal/atmosphere. Explanatory (or process-oriented) crop models comprise quantitative descriptions of the mechanisms and processes that cause the behavior of a system [1] These are based on bio-physical plant processes, simulating the diurnal effects of changes in the environment on plant growth as well as development. Environments with limited water and nutrition are included by using soil water balance modules including transpiration and nutrient (e.g., nitrogen, phosphor, and potassium) transformations in the soil as well as remobilization within the plants [2]
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