Abstract
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.
Highlights
Garlic (Allium sativum) is a historically important horticultural crop in many countries with global production reaching 30.7 million tons in 2019 after a 40 % increase in production in the last decade (FAOSTAT, 2020)
The original model was capable of simulating leaf area expansion at an individual leaf level and estimating carbon assimilated in a canopy calculated by coupled gas exchange, but assessing biomass allocated into a particular organ, i.e., bulb, was not a primary subject of the model at the time
Dataset 1 (RICCA Field) Our parameter for ND cultivar was first evaluated with measurements from field grown garlic in the dataset D1
Summary
Garlic (Allium sativum) is a historically important horticultural crop in many countries with global production reaching 30.7 million tons in 2019 after a 40 % increase in production in the last decade (FAOSTAT, 2020). Yield Estimation Using Garlic Model grown bulb is harvested for storage and the round of planting (Takagi, 1989; Kamenetsky, 2007). Some knowledge has been transferred to building crop models targeted for simulating garlic growth and estimating yield at harvest. An early attempt for building a whole-plant garlic model was based on radiation-use efficiency (RUE) to obtain the total amount of carbon assimilates (Rizzalli et al, 2002). Photosynthesis and transpiration responses to various environmental conditions were obtained for building a leaf-level gas-exchange model for garlic (Kim et al, 2013). Carbon partitioning is a crucial step in yield estimation modeling for horticultural crops in the sense that the final yield is a result of biomass partitioned into a certain organ, such as the bulb, to be harvested (Marcelis et al, 1998). The early garlic model used a set of multiple partitioning coefficients dynamically varying with developmental stages of the plant (Rizzalli et al, 2002)
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