18th Congress of the European Society for Agronomy

Using DSSAT to examine the interannual weather variation in forage maize productivity in Asturias and Galicia (Spain)

Not scheduled

15m

Les Dortoirs (1st floor) (The Couvent des Jacobins)

Poster Synergies between short- and long-term goals Poster session #2

Speaker

Prof. J.A. Oliveira (University of Oviedo)

Description

INTRODUCTION
Process-based crop models are excellent tools for quantifying the effects of management, genetics, soil and weather on growth, development, and yields (Addiscott and Wagenet, 1985). Oliveira et al. (2023) initiated the adaptation of the CSM-CERES-Maize model of software platform Decision Support System for Agrotechnology Transfer (DSSAT) (Hoogenboom et al., 2019) for forage maize and, to simulate growth and development of three forage maize cultivars for three sites of Asturias, Spain.
The objective of the present study was to use the CSM-CERES-Maize model in combination with the seasonal analysis of DSSAT to quantify the influence of interannual weather variation on forage maize production in Asturias and Galicia.

MATERIALS,METHODS
With the aim of examining the interannual variation in whole plant dry matter production, the model was executed with long-term historical meteorological data for 23 years from 2000 to 2022 with three cultivars (SE1-200, FAO-200; SE2-300, FAO-300; SE3-400, FAO-400) for three sites of Asturias and with three cultivars (XU1-200, FAO-200; XU2-300, FAO-300; XU3-400, FAO-400) for four sites in Galicia.
The genetic coefficients of the cultivars used in the simulation were obtained according to Oliveira et al. (2003).
For the simulations, the usual sowing and harvest dates for the trials in Asturias and Galicia were used.

RESULTS
The estimated genetic coefficients for three cultivars (SE1-200, SE2-300, SE3-400) for three locations in Asturias and for three cultivars (XU1-200, XU2-300, XU3-400) for four locations in Galicia for three years were the following:

P1 (ºC day): SE1-200 (145), SE2-300 (215), SE3-400 (230), XU1-200 (115), XU2-300 (160), XU3-400 (175).
P2 (Days): 0.3 for all the cultivars.
P5 (ºC day): SE1-200 (705), SE2-300 (650), SE3-400 (660), XU1-200 (590), XU2-300 (590), XU3-400 (580).
G2 (Nº grains): 650 for all the cultivars.
G3 (mg day-1): SE1-200 (6), SE2-300 (7), SE3-400 (7), XU1-200 (7), XU2-300 (8), XU3-400 (8).
PHINT (ºC day): 40 for all the cultivars.

As an example, the simulated mean values for the 23 years of meteorological data for dry matter production for each of the cultivars for the study sites in Asturias and Galicia were plotted (Figure 1).

Figure 1. Comparison of whole plant dry matter production (kg DM ha-1) simulated using 23 years of meteorological data in the seven locations for the six cultivars studied.

For all locations, the highest yield was achieved with the cultivars that had the longest growth cycle (SE3-400 and XU3-400) during the growing season. The average daily weather conditions for maximum temperatures ranged from 21.3 to 26.0 °C, for minimum temperatures it ranged from 9.8 to 15.8 °C, and for total solar radiation it ranged from 15.8 to 23.0 MJ m-2 day-1; total rainfall during the growing season ranged from 109.5 to 313 mm.

DISCUSSION
The CSM-CERES-Maize model was adapted to simulate forage maize yields by calibrating the genetic parameters of six cultivars: SE1-200, SE2-300 and SE3-400 in three locations and three years in Asturias, and XU1-220, XU2-300 and XU3-400 in four locations and three years in Galicia.
The calibration, together with the use of historical meteorological data (2000-2022) from the study sites, allowed simulation of the whole plant dry matter production of the six cultivars during the 23-year period. A specific model for forage maize is expected to be included in the DSSAT platform. Use of the new model should help to optimize management practices and harvest decisions in forage maize.

ACKNOWLEDGEMENTS
We are grateful for a grant provided by the “OECD Co-operative Research Programme” to support a research visit by the first author at the University of Florida, Gainesville, Florida, USA in 2022.

REFERENCES
-Addiscott, T.M., Wagenet, R.J., 1985. Concepts of solute leaching in soils: a review of modeling approaches. J. Soil Sci. 36:411–424.
-Hoogenboom, G., Porter, C.H., Boote, K.J., Shelia, V., Wilkens, P.W., Singh, U., White, J.W., Asseng, S., Lizaso, J.I., Moreno, L.P., Pavan, W., Ogoshi, R., Hunt, L.A., G.Y. Tsuji, G.Y., Jones, J.W., 2019. The DSSAT crop modeling ecosystem. In: pp.173-216 [K.J. Boote, editor] Advances in Crop Modeling for a Sustainable Agriculture. Burleigh Dodds Science Publishing, Cambridge, United Kingdom (http://dx.doi.org/10.19103/AS.2019.0061.10).
-Oliveira, J.A., Boote, K.J., Oliveira, F.A.A., Hoogenboom, G., Carballal, A., Martínez-Fernández, A., 2023. Adaptación del modelo CSM-CERES-Maize (DSSAT) para simular la producción de maíz forrajero: variación interanual en Asturias. Vaca Pinta, 40: 140-155.