Modeling interactions of planting date and phenology in Louisiana rice under current and future climate conditions

Abstract

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 CO2 level), and 2–3 months (to December) under extreme warming conditions (+4.1°C warmer and 133% higher CO2 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.

CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction

“…4,5 However, the simulation of crop growth models has great uncertainty on large spatial scales due to spatial differences in weather patterns, soil conditions, and management practices 6,7 and the limited availability of high-quality, region-specific data for model calibration and validation. [8][9][10] With the development of remote sensing technology, satellites can dynamically obtain high-quality images with a high spatial and temporal resolution near realtime. These quality images make it possible to extract and calculate relevant indicators, such as the normalized difference vegetation index (NDVI), enhanced vegetation index, crop water stress index, green vegetation index, and soil-adjusted vegetation index.…”