It may beĮxplained as mathematical or statistical representations of anyīiological entity (Haghverdi et al., 2014 Porter and Semenov,Ģ005). Quantitative application of cropīased model is called crop simulation modeling. They provide an efficient way to study complex biophysical Models are mathematical representations of real systems and We concluded that to bring accuracy in the simulation outcomes of models, new cultivars should be calibrated to minimize uncertainty to allow judicious recommendations in response to Of wheat cultivars while increased CO2 increased yield similar to the combined effect of increased temperature and CO2. Climate variability resultsĭepicted that an increase in temperature from 0 ☌ to 5 ☌ resulted in a 60% average decline in the yield Of crop phenology, LAI, biomass and grain yield against measured data. Evaluation with the measured data showed that performance of both models was realistic as indicated by the accurate simulation The temporal changes in maximum LAI accumulation for allĬultivars indicate that both measured and simulated values match each other. Model efficiency above 80% in most cases. Yield, with normalized root mean square error (RMSE) less than 10%, D-index greater than 0.80 and Both models wereĪble to accurately simulate anthesis and maturity days, maximum leaf area index, biomass and grain APSIM-Wheat and CERESWheat were calibrated for all five wheat cultivars using genetic coefficients estimated based upon measured data during 2008–09 cropping year and validated with independent data sets (experimental data ofĢ009 ––11 cropping seasons) which were not used for models calibration. The experiments were laid out in Randomized Complete Blockĭesign (RCBD) replicated four times with individual plot size of 5 m 3 m.
Origin namely Tatara, NARC-2009, Sehar-2006, SKD-1 and F-Sarhad were planted on 19th November, at Yield of five spring wheat cultivars under rainfed conditions in Pakistan.
We applied a manual method to calibrateĪPSIM-Wheat and CERES-Wheat for the flowering day, maturity day, leaf area index, biomass and grain Model calibration is necessary for application to new cultivars and environment. Ministry of Food Security and Research, Islamabad, PakistanĬrop growth in process based crop models is controlled by different parameters. Pakistan Agricultural Research Council (PARC), Institute of Advanced Studies in Agriculture, National Agricultural Research Centre, Islamabad, Pakistan Stöckle a, Gerrit Hoogenboom aĭepartment of Biological System Engineering, Washington State University, WA 99163, USAĭepartment of Agronomy, PMAS-Arid Agriculture University, Rawalpindi 46300, Punjab, Pakistan Mukhtar Ahmed a,b,⇑, Mustazhar Nasib Akram b, Muhammad Asim c, Muhammad Aslam d,įayyaz-ul Hassan b, Stewart Higgins a, Claudio O. Wheat under rainfed conditions: Models evaluation and application Farm management.Computers and Electronics in Agriculture 123 (2016) 384-401Ĭontents lists available at ScienceDirectĬalibration and validation of APSIM-Wheat and CERES-Wheat for spring Science > Statistics > Simulation modellingĪgriculture > Agriculture (General) > Farm economics. Item Type:Īgriculture > Agriculture (General) > Agricultural economics It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.
(2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. This paper updates the earlier work by Keating et al.
Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. (2003) described many of the fundamental attributes of APSIM in detail. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains.