Empirical and dynamic approaches for modelling the yield and N content of European grasslands

Dellar, Martha; Topp, Cairistiona F. E.; Pardo, Guillermo Orozco; Prado, A. del; Fitton, Nuala; Holmes, David; Banos, Georgios; Wall, E.

doi:10.1016/j.envsoft.2019.104562

Cited by 2 publications

(1 citation statement)

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“…The empirical models are simple, faster and therefore required less computation than process-based model and represent the interactions among variables without the correlated processes, sometimes known as statistical or correlative models [59,62]. An empirical crop model which simulating the yield of wheat crop as a function of weather conditions [63], estimating the yield as a function of water stress [64,65] was observed.…”

Section: Empirical Modelmentioning

confidence: 99%

Understanding the Agricultural Systems Modeling: A Review

2021

IJATCSE

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These Crop modeling is an imperative tool in complex agricultural systems, which has been achieved by scientists from a broad range of disciplines,who have provided ideas and tools for more than six decades. Agricultural researchers are now focusing on the "next-generation” models and information needed to solve the issues of complicated systems faced by the farming communities. It is essential to take stock of modeling history, methodology, and its suitability under different circumstances to guarantee that we avoid re-development and endeavor to consider all elements of associated challenges. In this paper, we review the historical connectivity andthe methodology of agricultural systems modeling and identify various modeling approaches to guide the design and advancement of “next-generation” modeling tools and techniques. Historical events combined with technological advancement have powerfully contributed towards the development of agricultural and dynamic models at domestic to global scales. Agricultural systems models have wide characteristics relying on the systems involved, purposes, and the broad applications that intended their development and use by scientists in numerous fields. Recently the interdisciplinary research and public-private sectors partnership trends suggest that the stage is set for key advancement in agricultural systems science which is required for the “next-generation” models, information, databases, and decision support systems. Historical events and conceptual methodologies of agricultural system modeling should be considered to avoid barricades and pitfalls as the community build up these “next-generation” crop models.

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