Strigolactones (SLs) are a new type of plant endogenous hormones that have been found to regulate plant growth and architecture.  At present, some genes related to the biosynthesis and signaling pathway of SLs have been isolated in plants such as Arabidopsis thaliana, Pisum sativum and Oryza sativa.  However, the signaling pathway and specific mechanism of SLs in cotton remain unclear.  In this study, we identified the SLs signaling gene GhMAX2 and demonstrated its function in plant growth and architecture in Gossypium hirsutum.  Bioinformatics analysis showed that GhMAX2 mainly consists of an α-helix and a random coil and includes a large number of leucine-rich repeats.  GhMAX2 was highly expressed in root, stem, flower, and fibers at 20 days post-anthesis (DPA).  GhMAX2 promoter-driven β-glucuronidase expression was present exclusively in the root, main inflorescence, flower, and silique.  Subcellular localization showed that GhMAX2 is targeted to the nucleus.  Heterologously expressed GhMAX2 can rescue the phenotype of Arabidopsis max2-1 mutant, indicating that the function of MAX2 is highly conserved between G. hirsutum and A. thaliana species.  In addition, the knockdown expression of GhMAX2 in cotton resulted in significantly reduced plant height, slow growth, short internodes, and reduced fiber length.  These findings indicate that GhMAX2 probably contributes to plant growth, architecture and fiber elongation in cotton. The study reveals insights into the roles of GhMAX2-mediated SL/KAR signaling in cotton and provides a valuable foundation for the cultivation of cotton plants in the future.

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale.  To verify this method, we applied the ensemble Kalman filter (EnKF) to assimilate the leaf area index (LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model.  From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi.  We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat.  We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI.  With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat.  We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error (RMSE) by 0.43 and 0.29 m² m^–2, respectively, based on the measured LAI.  The assimilation improved the estimation accuracy of the LAI time series.  The highest determination coefficient (R₂) was 0.8627 and the lowest RMSE was 472.92 kg ha^–1 in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements.  The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with
high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.