Institute of Convergence Technology, KT, Seoul, 06763, Korea.
Department of Plant Science, Seoul National University, Seoul, 08826, Korea.
Sci Rep. 2018 Jul 5;8(1):10220. doi: 10.1038/s41598-018-28291-3.
The Law of the Minimum is often implemented using t-norm or fuzzy intersection. We propose the use of t-conorm or fuzzy union for climate suitability assessment of a grass species using annual ryegrass (Lolium multiflorum Lam.) as an example and evaluate the performance for alfalfa (Medicago sativa L.) and sorghum (Sorghum bicolor L.). The OR and AND models, which are fuzzy logic systems based on t-conorm and t-norm between temperature and moisture conditions, respectively, were developed to assess the quality of climate conditions for crops. The parameter values for both models were obtained from existing knowledge, e.g., the EcoCrop database. These models were then compared with the EcoCrop model, which is based on the t-norm. The OR model explained greater variation (54%) in the yield of annual ryegrass at 84 site-years than the AND model (43%) and the EcoCrop model (5%). The climate suitability index of the OR model had the greatest likelihood of occurrence of annual ryegrass compared to the other models. The OR model also had similar results for alfalfa and sorghum. We emphasize that the fuzzy logic system for climate suitability assessment can be developed using knowledge rather than presence-only data, which can facilitate more complex approaches such as the incorporation of biotic interaction into species distribution modeling.
最小定律通常使用 t-范数或模糊交用来实现。我们建议使用 t-余范数或模糊并集来评估一年生黑麦草（Lolium multiflorum Lam.）的气候适宜性，并评估紫花苜蓿（Medicago sativa L.）和高粱（Sorghum bicolor L.）的性能。基于温度和湿度条件之间的 t-余范数和 t-范数的模糊逻辑系统 OR 和 AND 模型分别被开发出来，以评估作物气候条件的质量。这两个模型的参数值均来自现有知识，例如 EcoCrop 数据库。然后将这两个模型与基于 t-范数的 EcoCrop 模型进行比较。与 AND 模型（43%）和 EcoCrop 模型（5%）相比，OR 模型可以更好地解释 84 个地点-年的一年生黑麦草产量变化（54%）。与其他模型相比，OR 模型的气候适宜指数更有可能出现一年生黑麦草。OR 模型对于紫花苜蓿和高粱也有类似的结果。我们强调，使用知识而不是仅存在数据来开发气候适宜性评估的模糊逻辑系统，可以促进更复杂的方法，例如将生物相互作用纳入物种分布模型。
