Trends in soil moisture content and water deficits in Argentina and the role of climate contribution

Peretti, M.; Spennemann, Pablo C.; Long, María Elena Fernández

doi:10.1007/s00704-023-04428-x

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The available soil water is the difference between the amount of SM t and the amount of PWP. This model has been proven to have a very good fit with observed data (Fernández-Long et al, 2018;Spennemann et al, 2020) and has been used in Argentina for the estimation of soil moisture with different objectives (Fernández-Long et al, 2021;Penalba et al, 2019;Peretti et al, 2023;Pinto et al, 2017).…”

Section: Available Soil Water Estimationmentioning

confidence: 99%

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

Severini,

Álvarez-Prado,

Otegui

et al. 2023

Preprint

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The phenology of 34 soybean varieties covering a broad range of maturity groups (2.2-6.8) was evaluated in a series of 43 field experiments located in Argentina, Uruguay, and Paraguay. The sowing dates ranged from middle October to end of January (spring and summer months in the Southern hemisphere) and the site latitudes ranged from -24.9 to -35.6, providing a wide range of thermo-photoperiodic conditions during the growing season. Soybean genotypes exhibited wide variation in the days from sowing to specific developmental stages, mostly associated with photoperiod sensitivity. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across maturity groups. The final model was made available at http://cronosoja.agro.uba.ar/. We identified base temperatures specific for developmental stages and an extra parameter for calculating the photoperiod multiplier after R1 (flowering) stage. Also, a critical photoperiod length for each maturity group was derived. Model selection showed that the determinants of phenology across maturity groups were mainly affecting early reproductive stages. The model's ability to predict phenology was more robust for early than for late stages of development, especially for sites with lower growing-season temperatures. Therefore, further research is needed to improve the model predictions at low temperatures. Overall this study provides valuable information on the relationship between soybean phenology and environmental factors, which could help to improve the accuracy of simulation models predicting soybean growth and yield in different regions.

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Section: Available Soil Water Estimationmentioning

confidence: 99%

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

Severini,

Álvarez-Prado,

Otegui

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

Severini,

Álvarez-Prado,

Otegui

et al. 2024

In Silico Plants

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Accurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in field experiments located in Argentina, Uruguay, and Paraguay. Experiments covered a broad range of maturity groups (2.2-6.8), sowing dates (from spring to summer), and latitude range (24.9-35.6 °S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across maturity groups. We identified base temperatures specific for different developmental phases and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each maturity group was found. Model selection showed that the determinants of phenology across maturity groups were mainly affecting the duration of vegetative and early reproductive phases. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. In summary, we have constructed a soybean phenology model that simulates phenology accurately across various geographic locations and sowing dates. The model's process-based approach has resulted in root mean square errors ranging from 5.8 to 9.5 days for different developmental stages. The final model was made available at http://cronosoja.agro.uba.ar.

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Trends in soil moisture content and water deficits in Argentina and the role of climate contribution

Cited by 2 publications

References 31 publications

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

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