Comparison of process-based and statistical approaches for simulation and projections of rainfed crop yields

Mohammad Reza Eini, Haniyeh Salmani and Mikołaj Piniewski

Agricultural Water Management, 2023, vol. 277, issue C

Abstract: Accurate and comprehensive modelling aimed at investigating the impact of climate change on rainfed crop yields is of great importance due to the interconnected issues of water scarcity and food security. Because the process-based and statistical approaches to simulating crop yields are different in nature, a comparison between them is needed. This study investigates the accuracy of crop yield simulations in the historical period as well as future projections using two modelling approaches: 1) a process-based approach employing the Soil and Water Assessment Tool+ (SWAT+) model, and 2) a statistical approach employing a data-driven model, Feed Forward Back Propagation Neural Network (FFBPNN) over a medium-sized catchment in north-western Poland. The application of two potential evapotranspiration methods (Penman-Monteith and Hargreaves) in SWAT+ permitted calibration (2004–2011) and validation (2012–2019) of runoff and yields of winter wheat and spring barley. Different combinations of climatic parameters with a drought index based on Joint Deficit Index were applied to simulate and project rainfed crop yields (winter wheat, barley, potato, rye, rapeseed, sugar beets, cereals, maize for grain, maize for green forage, pulses) with FFBPNN. The results reveal that adding the new drought index helped increase the FFBPNN performance. This approach showed that future yields of the studied crops would slightly increase under RCP8.5 by 2060. Winter wheat and spring barley projections from SWAT+ showed very small changes using both the Penman-Monteith and Hargreaves method. Policy-wise, the results should be of interest to climate change adaptation practitioners and food security experts. Future studies should aim at more thorough investigation of the role of the downscaling technique and extreme events, as well as the effect of elevated CO2 on future crop yields.

Keywords: Global warming; Oder River basin; Baltic Sea basin; Machine learning; Artificial Neural Network (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:277:y:2023:i:c:s0378377422006540

DOI: 10.1016/j.agwat.2022.108107

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