Major crop models were developed before the 1990s and many of their algorithms are (semi-)empirical. In the recent two decades, the number of models has grown rapidly, but the increase in their quality does not match the growth in number. Often, an ensemble of multiple models is required to make a useful prediction. This ensemble approach does not facilitate the improvement of models as a tool to understand crop physiological mechanisms. On the other hand, following a bottom-up approach that tries to assemble all elements along different scales of biological organisation may result in numerically clumsy models that are not necessarily more robust than existing models in simulating phenotypes at the crop scale.

We argue that to model complex crop phenotypes in a simple yet accurate manner, crop modellers should be inspired by experiences in some fundamental sciences. For example, physicists used sound theories and solid mathematics in thought experiments, and came up with seemingly simple equations to explain the behaviour of very diverse systems, from sub-atomic particles to the largest clusters of galaxies.

We review examples, where biological insights and mathematics are combined to derive simple equations that apply to different processes of crop growth. The essence of this modelling approach is a combination of simplicity, elegance and robustness. Models integrating those equations can predict crop phenotypes as well as generate hypotheses or emerging properties to assist in knowing the unknowns. Thus, crop models should not only provide practical predictions, but should especially be considered as a research tool for data interpretation, system design, and heuristic understanding.

主要作物模型是在 1990 年代之前开发的，它们的许多算法都是（半）经验的。近二十年来，模型的数量增长迅速，但其质量的提高并不与数量的增长相匹配。通常，需要多个模型的集合才能做出有用的预测。这种集成方法不利于改进模型作为理解作物生理机制的工具。另一方面，采用自下而上的方法，试图将所有元素沿着不同的生物组织尺度组装起来，可能会导致数值笨拙的模型在模拟作物规模的表型时不一定比现有模型更强大。

我们认为，要以简单而准确的方式对复杂的作物表型进行建模，作物建模者应该受到一些基础科学经验的启发。例如，物理学家在思想实验中使用了可靠的理论和坚实的数学，并提出了看似简单的方程来解释从亚原子粒子到最大的星系团的非常多样化系统的行为。

我们回顾了一些例子，在这些例子中，生物学见解和数学相结合，推导出适用于不同作物生长过程的简单方程。这种建模方法的本质是简单、优雅和稳健的结合。整合这些方程的模型可以预测作物表型，并生成假设或新出现的特性，以帮助了解未知数。因此，作物模型不仅应提供实用的预测，而且应特别考虑作为数据解释、系统设计和启发式理解的研究工具。