Salmeron Cortasa, Montserrat

Parameter optimization of crop eco-physiological models for agronomic applications

(updated 2/23/2023)

Through the high performance computing facility we are able to parametrize crop-eco physiological models that simulate carbon, water, and N dynamics with detailed experimental data. Our research is focused on applying calibrated models to study management and genotype adaptations and crop rotations that increase crop productivity with a sustainable use of water and nutrient resources. We are also interested in integrating aerial imaging data in model calibrations to reduce model uncertainty, and in model development for improving simulation of crop nitrogen dynamics in soybean. We will use the high performance computing facility to calibrate different crop and soil parameters within the DSSAT (Decision Support System for Agrotechnology Transfer, https://dssat.net/) crop model simulation software.

Personnel:

Montse Salmeron Cortasa (PI)

Students:

Mounica Talasila (PhD student), Added on MCC cluster, 02/23/2023

Leonardo Monteiro (Postdoctoral scholar), Added on MCC cluster, 02/23/2023

Mariely Lopes dos Santos (Postdoctoral scholar)

Patricia Moreno Cadena, Postdoc, Added on MCC cluster, 3/14/2023 

Mohammad Shamim, Postdoc, Added on MCC cluster, 04/04/2023 

Software:

(free and/or open source) DSSAT, R, cmake, gcc

Ecophysiology of soybean yield and water use efficiency – experimental and modeling approaches

Soybean planting date and cultivar choice are management decisions with the largest impact on yield potential and that will influence total crop water requirements. Optimized crop management decisions tailored to different environments and water availability are therefore essential in order to meet an increasing food demand with a sustainable use of water. A mechanistic crop simulation model will be used to investigate genotype x environment x management options that maximize yield and water use efficiency. The DSSAT-CROPGRO-Soybean model will be evaluated with field experiments comprising the range of cultivar maturities and planting date options in KY. Cultivar coefficients for the model will be calibrated by optimization using a stepwise procedure with data collected at harvest while considering end of season traits determined earlier in the growing season.

Personell:

Montse Salmeron Cortasa (PI), Maria Morrogh Bernard (Master student), Blazan Mijtavic (Research analyst)

Software:

(free and/or open source) DSSAT, R

Genotypic and phenotypic characterization of VRN and PPD alleles in soft red winter wheat

Selection of soft red winter wheat cultivars best adapted to different climatic conditions is critical for reducing risk of spring freeze and allowing an early harvest suitable for double cropping in Kentucky. A total of 50 soft red winter wheat lines with different vernalization (Vrn) and photoperiod (Ppd) alleles were genotyped using KASP markers. Field experiments with a range of planting dates and vernalization treatments were used to phenotype the timing of developmental stages for the 50 lines. The DSSAT crop simulation model platform will be used to simulate key developmental stages across the different experimental conditions using three different wheat models (Cropsim-CERES-Wheat, Cropsim-Wheat, and APSIM-Wheat). Cultivar coefficients related to photoperiod and vernalization sensitivity will be obtained for each of the 50 lines by optimization. The relationship between optimized cultivar coefficients from the different models and the genotypic information obtained by the KASP markers will be investigated.

Personell:

Montse Salmeron Cortasa (co-PI), Ethan Snyder (Master student), David Van Sanford (co-PI), Carrie Knott (PI)

Software:

(free and/or open source) DSSAT, R