Evaluating differences among crop models in simulating soybean in-season growth

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F24%3A00585466" target="_blank" >RIV/86652079:_____/24:00585466 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0378429024000595?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0378429024000595?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.fcr.2024.109306" target="_blank" >10.1016/j.fcr.2024.109306</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating differences among crop models in simulating soybean in-season growth

Popis výsledku v původním jazyce

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and ,,blind,, evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8-31% after Full calibration). However, the nRMSE for pod weight (of 17-64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47-93 days) and effective seed filling period (18-54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.

Název v anglickém jazyce

Evaluating differences among crop models in simulating soybean in-season growth

Popis výsledku anglicky

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and ,,blind,, evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8-31% after Full calibration). However, the nRMSE for pod weight (of 17-64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47-93 days) and effective seed filling period (18-54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Field Crops Research

ISSN

0378-4290

e-ISSN

1872-6852

Svazek periodika

309

Číslo periodika v rámci svazku

APR

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

109306

Kód UT WoS článku

001201828400001

EID výsledku v databázi Scopus

2-s2.0-85186497821