Adam Famoso

‘CLL17’ (Reg. no. CV‐160; PI 693941), a high‐yielding long‐grain rice ( Oryza sativa L.) cultivar, was developed by the Louisiana State University Agricultural Center at the H. Rouse Caffey Rice Research Station near Crowley, LA, and approved for release in 2020. CLL17 contains the herbicide‐resistant gene for use in the Clearfield rice production system. The Clearfield rice production system is defined as the brand of rice cultivars that are tolerant to imidazolinone herbicides. CLL17 was derived from the cross ‘CL131’/‘Trenasse’. It showed good yield potential and favorable agronomic characteristics in the 2015 Clearfield Preliminary Yield Trial. In 2016, it was evaluated in the uniform regional rice nursery trial across four southern states. From 2017 to 2022, CLL17 was evaluated in 50 replicated trials across 10 locations in Louisiana. Three high‐yielding commercial cultivars were included as checks: ‘Cheniere’, ‘CL153’, and ‘CL111’. The grain yield of CLL17 was 9.0 t ha −1 compared with 8.9 t ha −1 for CL153, 8.3 t ha −1 for CL111, and 8.2 t ha −1 for Cheniere across 25 commercial advanced trials from 2017 to 2020. CLL17 had a similar plant height to CL111, 2‐cm taller than CL153, and 5‐cm taller than Cheniere. It exhibits typical US long‐grain rice cooking quality with intermediate amylose and gel temperature. CLL17 is susceptible to sheath blight [caused by Rhizoctonia solani Kuhn ], resistant to Cercospora spp., leaf blast [caused by Magnaporthe grisea (T.T. Hebert)], and neck blast, and moderately susceptible to bacterial panicle blight (caused by Burkholderia glumae ).

The performance of novel genetic combinations under untested environmental scenarios and management practices can be virtually examined using process‐based crop models. Indeed, there has been a long‐standing interest in the crop modeling community to expand the utility of process‐based models to broader germplasm panels (e.g., breeding lines or diversity panels). Yet, there is often a misalignment between data needed to parameterize process‐based crop models and data routinely collected by breeding programs. To address this gap, we leverage a dataset from a long‐term trial on advanced experimental lines and released varieties from the Louisiana rice breeding program to calibrate and evaluate the decision support for agrotechnology transfer (DSSAT) CSM‐CERES‐Rice model. Next, we use data collected by the same program on a large collection of breeding lines to generate numerous in silico genotypes and evaluate their performance across different management practices (different planting dates) and three climatic conditions (current climate and two future scenarios based on CMIP6‐SSP5‐8.5 climate projections). Our simulations indicate that shifting the current planting date (i.e., March) back by 1–2 months (to January) under moderate warming conditions (+1.3°C warmer and 41% higher CO 2 level), and 2–3 months (to December) under extreme warming conditions (+4.1°C warmer and 133% higher CO 2 level) could potentially offset the negative impacts of the increased future temperature. Given earlier planting, shorter duration varieties (i.e., those with shorter growing degree day requirements during the vegetative and grain filling periods) are found to be more favorable for supporting high yields. Such varieties with a shorter thermal time to anthesis are found to remain just outside of the current pool of variation for this trait. Opportunities and challenges for leveraging breeding data in process‐based modeling to derive insights into adaptation strategies for future climates are further discussed.