Community forest management – A case study of East Kameng district, Arunachal Pradesh, India, Consultancy report, CFMWG-NEI, NEHU, Shillong, India and Community Forestry International Inc., Santa Barbara, USA, 2004. Principles of Forecasting: A Handbook for Researchers and Practitioners (Section 8: “Neural Networks for Time-Series Forecasting”). Cambridge University Press, Cambridge, UK, 2006. Engineering Psychology and Human Performance (2nd Edition). Lap Lambert Academic Publishing, Germany,2012, pp-148. GIS Assisted Farm Management Information System. Philosophical Transaction of Royal Society of London, 1997, vol.: 352, pp: 1121-1128. In: Fernandex-Ballesteros R (Eds.) Encyclopedia of psychological assessment. Sustainable agricultural development demands multidisciplinary holistic approach and intelligence should be the guiding principle that demands study of human cognitive psychology. revolutionize the whole agricultural sector opening new and competent options and dimensions. The intelligent techniques like forecasting, database management, knowledge discovery, deception, simulation, contingency planning etc. The objective of the current paper is to analyze the attributes that are considered to be characteristics of intelligence having wide potential for the development of intelligent system and technologies for agricultural applications. Intelligence is that resource that guides actions and provide options under variable, uncertain and unseen conditions. Agriculture is emerging as knowledge-based enterprise that demands efficient need-based information retrieval systems and smart actions. The novelty or ambiguity that the variable environment presents, demands for the development of self-adaptive intelligent systems in agriculture and allied sectors. The pace of current climate change, which is unique about it, makes the biological system more and more complicated and unpredictable. Therefore, it can be concluded, that both APSIM and CERES models seem to be suitable for simulating wheat crop parameters in Guanzhong Plain based on calibration, evaluation, sensitivity analysis and ensemble model approach.Biological systems, including agriculture and allied sectors are very complex and nonlinear in nature. Mean ensemble model results showed reduced uncertainty for different crop parameter, especially the grain yield differences were reduced by 0.73-4.1% compared to observed yield. Sensitivity analysis of both models showed that grain yield in CERES-Wheat was more sensitive to field capacity and in APSIM grain yield was quite sensitive to nitrogen application rate. APSIM and CERES-Wheat during the evaluation simulated aboveground biomass, grain yield, LAI, canopy nitrogen, cumulative evapotranspiration, water use efficiency and nitrogen fertilizer productivity, and deviations from the observed were reasonable with nRMSE less than 21%. During calibration, APSIM grain yield was underestimated than CERES-Wheat while biomass and leaf area index was overestimated than CERES-Wheat model. APSIM and CERES-Wheat model calibration results of grain yield, biomass, and leaf area index varied between 1.4-14.1 % during three growing seasons. Experimental data was collected from a past study conducted in the same Guanzhong Plain where different irrigation and nitrogen levels treatments were tested during wheat growing season of 2009-2010, 12. Models performance was evaluated based on the criteria of statistical analysis including coefficient of determination (R 2), normalized root mean square error (nRMSE), and d-index. Crop models were calibrated by adopting standard procedure and protocols. In this study, two process based crop models APSIM and CERES-Wheat models were selected to determine their feasibility to simulate the wheat crop production in Guanzhong Plain in China by using calibration, evaluation and sensitivity analysis with experimental local data. These crop models are calibrated and evaluated before applying to a new geographical location. Cropping system models are considered useful tools to estimate the impact of climate and environment on agriculture production, and to improve the management of agricultural systems. Keywords: Calibration, Evaluation, Multiple crop models, Sensitivity analysis, Wheat yieldĪbstract. Joseph, Michigan Citation: 2019 ASABE Annual International Meeting 1900416.(doi:10.13031/aim.201900416)Īuthors: Qaisar Saddique, Jianmei Ji, Ali Ajaz, Xu Jiatun, Zou Yufeng, Jianqiang He, Huanjie Cai Published by the American Society of Agricultural and Biological Engineers, St. Performance Comparison of the APSIM and CERES-Wheat models in Guanzhong Plain, China If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.
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