Abstract
The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001–2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha−1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions.
Highlights
The expected world population growth, the limited availability of arable land, and the impacts of climate change on cereal production indicate the need to increase the quantity and quality of global grain production to meet the growing demand of food and dietary requirements [1]
This study aims to contribute to the available literature on crop model parameterization to simulate durum wheat (Triticum durum Desf.), common wheat (Triticum aestivum L.), and maize (Zea mays L.) using Crop Simulation Models (CSMs)-CERES-Wheat and CSM-CERES-Maize models and multi-site and multi-year observations
The results of this study offer a set of Cultivar-Specific Parameters (CSPs) for CSM-CERES-Wheat and CSM-CERES-Maize, successfully tested over a large dataset of experimental observations for anthesis date and grain yield of durum and common wheat and grain yield of maize
Summary
The expected world population growth, the limited availability of arable land, and the impacts of climate change on cereal production indicate the need to increase the quantity and quality of global grain production to meet the growing demand of food and dietary requirements [1]. CSMs have become agricultural system models that incorporate the capability to analyze a variety of issues, including changes in soil carbon, greenhouse gas emissions, plant breeding, resource use and efficiency, ecosystem services, pests and diseases, food security, yield-gap analysis, and climate change mitigation and adaptation [3] to support the decision making process They can be applied as “what if” tools, in addition to field and farm experiments that require large amounts of time and resources to support farmers and policy makers to manage agricultural systems under different conditions, and provide guidelines for a sustainable agricultural management with environmental, social, and economic benefits
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