Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria.

J Agric Sci

Republic Hydrometeorological Service of Serbia, Kneza Višeslava 66, 11000 Belgrade, Serbia.

Published: July 2018

AI Article Synopsis

  • The study presents a probabilistic crop forecast using ensemble crop model outputs to estimate green water components, crop yields, and green water footprints for summer crops like maize, spring barley, and sunflower.
  • Seasonal forecasts were made using the AquaCrop model and weather data from the European Centre for Medium-range Weather Forecast, focusing on crop water use indicators and yield accuracy.
  • Results show that sunflower in Novi Sad, Serbia, and maize in Groß-Enzersdorf, Austria, had the best predictions with an average root mean square error of less than 10%, while the Ignorance score assessed the accuracy of probability distributions in predictions for various crops and locations.

Article Abstract

A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) - average root mean square error (2006-2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199546PMC
http://dx.doi.org/10.1017/S0021859618000047DOI Listing

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