Data_Sheet_1_An Integrative Process-Based Model for Biomass and Yield Estimation of Hardneck Garlic (Allium sativum).PDF

We introduce an integrative process-based crop model for garlic (Allium sativum). Building on our previous model that simulated key phenological, morphological, and physiological features of a garlic plant, the new garlic model provides comprehensive and integrative estimations of biomass accumulation and yield formation under diverse environmental conditions. This model also showcases an application of Cropbox to develop a comprehensive crop model. Cropbox is a crop modeling framework featuring declarative modeling language and a unified simulation interface for building and improving crop models. Using Cropbox, we first evaluated the model performance against three datasets with an emphasis on biomass and yield measured under different environmental conditions and growing seasons. We then applied the model to simulate optimal planting dates under future climate conditions for assessing climate adaptation strategies between two contrasting locations in South Korea: the current growing region (Gosan, Jeju) and an unfavorable cold winter region (Chuncheon, Gangwon). The model simulated the growth and development of a southern-type cultivar (Namdo, ND) reasonably well. Under Representative Concentration Pathway (RCP) scenarios, an overall delay in optimal planting date from a week to a month, and a slight increase in potential yield were expected in Gosan. Expansion of growing region to northern area including Chuncheon was expected due to mild winter temperatures in the future and may allow ND cultivar production in more regions. The predicted optimal planting date in the new region was similar to the current growing region that favors early fall planting. Our new integrative garlic model provides mechanistic, process-based crop responses to environmental cues and can be useful for assessing climate impacts and identifying crop specific climate adaptation strategies for the future.

本团队提出了一种基于综合过程导向的大蒜（Allium sativum）作物模型。该模型在先前构建的，能够模拟大蒜植株关键物候、形态和生理特性的模型基础上，进一步提供了在大蒜生物量积累和产量形成过程中的全面且综合的估算，并能够适应多样的环境条件。此模型亦展示了Cropbox框架在构建综合作物模型中的应用。Cropbox是一种具有声明性建模语言和统一仿真接口的作物模拟框架，旨在构建与优化作物模型。借助Cropbox，我们首先针对三个数据集评估了模型性能，重点关注在不同环境条件和生长季节下测量的生物量和产量。随后，我们将模型应用于模拟未来气候条件下最佳种植日期，以评估韩国两处截然不同的地点——当前生长区（高赞，济州岛）和不利的寒冷冬季地区（春川，江原道）之间的气候适应策略。模型对南方品种（南道，ND）的生长和发育进行了合理的模拟。在代表性浓度路径（RCP）情景下，预计高赞地区的最佳种植日期将推迟一周至一个月，潜在产量略有上升。由于未来冬季气温的温和，预计种植区域将扩展至包括春川在内的北部地区，这或许将允许ND品种在更多地区进行种植。在新区域的预测最佳种植日期与有利于早秋种植的当前生长区相似。我们提出的新综合大蒜模型能够提供基于机制的、过程导向的作物对环境信号的响应，对于评估气候影响和识别未来针对特定作物的气候适应策略具有重要意义。