An update of new flood-irrigated rice cultivars in the SimulArroz model

Ribas, Giovana Ghisleni; Streck, Nereu Augusto; Duarte Junior, Ary José; Ribeiro, Bruna San Martin Rolin; Pilecco, Isabela Bulegon; Rossato, Ioran Guedes; Richter, Gean Leonardo; Bexaira, Kelin Pribs; Pereira, Vladison Fogliato; Zanon, Alencar Junior

doi:10.1590/S1678-3921.pab2020.v55.00865

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AGROMETEOROLOGY • Pesq. agropec. bras. 55 • 2020 • https://doi.org/10.1590/S1678-3921.pab2020.v55.00865 copy

An update of new flood-irrigated rice cultivars in the SimulArroz model

Uma atualização de novas cultivares de arroz irrigado por inundação no modelo SimulArroz

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract:

The objective of this work was to model, in the SimulArroz model, the three flood-irrigated rice (Oryza sativa) cultivars currently most grown in the state of Rio Grande do Sul, Brazil. The experiments to calibrate and validate the model were conducted in the municipalities of Cachoeirinha, Santa Maria, Uruguaiana, Santa Vitória do Palmar, and Cachoeira do Sul during four crop seasons. The number of leaves, phenology, aboveground dry matter biomass, and yield of each cultivar were evaluated. The results showed a slight overestimate of the R1, R4, and R9 stages; however, overall, the SimulArroz model had a good performance in simulating rice phenology for the three studied genotypes. Furthermore, the model had a reasonable accuracy in simulating aboveground dry matter and yield. The root-mean-square error (RMSE) for aboveground dry matter (leaves, stems, panicles, and grains) ranged from 0.5 to 3.0 Mg ha^-1. For yield, the RMSE ranged from 0.8 to 1.3 Mg ha^-1. The calibration of the SimulArroz model is efficient in simulating the growth, development, and grain yield of the most important flood-irrigated rice cultivars in Southern Brazil and can be used to estimate harvest forecast and yield potential, as well for yield gap studies.

Index terms:
Oryza sativa; mathematical model; yield

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[1] *Corresponding author: giovana.ghisleni@hotmail.com

Data		Calibration		Evaluation
Experiment (site)	Soil type (clay %)	n⁽¹⁾ (sowing date range)	n (cultivar maturation group)⁽²⁾	n (sowing date range)	n (cultivar maturation group)
Cachoeirinha (-29°95'S, -51°12'W, at 17 m)	Typic Albaqualf (6%)	2 (Oct. 01 to Nov. 09)	3 (early - intermediate cycle)	33 (Oct. 18 to Dec. 15)	3 (early - intermediate cycle)
Cachoeira do Sul (-30°02'S, -52°53'W, at 68 m)	Typic Albaqualf (6%)	-	-	20 (Sept. 01 to Dec.15)	3 (early - intermediate cycle)
Santa Vitória do Palmar (-33^o51'S, -53^o35'W, at 24 m)	Typic Albaqualf (6%)	-	-	16 (Sept. 01 to Dec. 15)	3 (early - intermediate cycle)
Santa Maria (-29^o72'S, -53^o71'W, at 95 m)	Typic Hapludalf (43%)	-	-	15 (Oct. 24 to Dec. 03)	3 (early - intermediate cycle)
Uruguaiana (-29^o83'S, -57^o08'W, at 74 m)	Lithic Udorthent (39%)	-	-	23 (Sept. 01 to Dec. 15)	3 (early - intermediate cycle)

Parameter⁽¹⁾	Unit	Cultivar
Parameter⁽¹⁾	Unit	IRGA 424 RI	Guri INTA CL	Puitá INTA-CL
LAR_{max 1,2}	Leaves per day	0.272	0.276	0.276
TTEM	˚C day	80.0	80.0	80.0
TTVG	˚C day	659.2	581.6	591.4
TTRP	˚C day	168.4	134.6	156.85
TTEG	˚C day	108.7	95.9	89.81
RUE	g MJ^-1	2.87	2.65	2.77
LAI	-	8.3	8.3	6.46
SOCF	Spikelets per gram MS	70.0	90.0	90.0
Pmax	Grams	0.0232	0.0245	0.0245

DVS⁽¹⁾	Aboveground dry matter	IRGA 424 RI			Guri INTA CL			Puitá INTA-CL			Hybrid
DVS⁽¹⁾	Aboveground dry matter	L	S	P	L	S	P	L	S	P	L	S	P
0	0.5	0.73	0.27	0.0	0.7	0.30	0.0	0.75	0.30	0.0	0.4	0.6	0.0
0.43	0.75	0.5	0.5	0.0	0.6	0.4	0.0	0.48	0.52	0.0	0.4	0.6	0.0
0.75	0.75	0.3	0.7	0.0	0.2	0.8	0.0	0.20	0.80	0.0	0.3	0.7	0.0
1.0	1.0	0.0	0.9	0.1	0.0	0.8	0.2	0.0	0.80	0.2	0.1	0.3	0.6
1.2	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
1.6	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0
2.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0

RMSE⁽¹⁾ (Mg ha^-1)	NRMSE⁽²⁾ (%)	Model, country, and reference
Aboveground dry matter
0.5	20	Oyza2000, Philippines, Boling et al. (2011)BOLING, A.A.; BOUMAN, B.A.M.; TUONG, T.P.; KONBOON, Y.; HARNPICHITVITAYA, D. Yield gap analysis and the effect of nitrogen and water on photoperiod-sensitive Jasmine rice in north-east Thailand. Wageningen Journal of Life Sciences, v.58, p.11-19, 2011. DOI: https://doi.org/10.1016/j.njas.2010.05.001. https://doi.org/10.1016/j.njas.2010.05.0...
2.2	19	Oyza2000, Chile, Artacho et al. (2011)ARTACHO, P.; MEZA, F.; ALCALDE, J.A. Evaluation of the Oryza2000 rice growth model under nitrogen-limited conditions in an irrigated Mediterranean environment. Chilean Journal of Agricultural Research, v.71, p.23-33, 2011. DOI: https://doi.org/10.4067/S0718-58392011000100003. https://doi.org/10.4067/S0718-5839201100...
0.8	12	InfoCrop, India, Aggarwal et al. (2006)AGGARWAL, P.K.; KALRA, N.; CHANDER, S.; PATHAK, H. InfoCrop: a dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description. Agricultural Systems, v.89, p.1-25, 2006. DOI: https://doi.org/10.1016/j.agsy.2005.08.001. https://doi.org/10.1016/j.agsy.2005.08.0...
0.7	-	RiceGrow, China, Tang et al. (2009)TANG, L.; ZHU, Y.; HANNAWAY, D.; MENG, Y.; LIU, L.; CHEN, L.; CAO, W. RiceGrow: a rice growth and productivity model. Wageningen Journal of Life Sciences, v.57, p.83-92, 2009. DOI: https://doi.org/10.1016/j.njas.2009.12.003. https://doi.org/10.1016/j.njas.2009.12.0...
0.8	-	Oryza2000, China, Tang et al. (2009)TANG, L.; ZHU, Y.; HANNAWAY, D.; MENG, Y.; LIU, L.; CHEN, L.; CAO, W. RiceGrow: a rice growth and productivity model. Wageningen Journal of Life Sciences, v.57, p.83-92, 2009. DOI: https://doi.org/10.1016/j.njas.2009.12.003. https://doi.org/10.1016/j.njas.2009.12.0...
2.5	27	SimulArroz, Brazil, present study
Leaf dry matter
0.9	47	Oyza2000, Chile, Artacho et al. (2011)ARTACHO, P.; MEZA, F.; ALCALDE, J.A. Evaluation of the Oryza2000 rice growth model under nitrogen-limited conditions in an irrigated Mediterranean environment. Chilean Journal of Agricultural Research, v.71, p.23-33, 2011. DOI: https://doi.org/10.4067/S0718-58392011000100003. https://doi.org/10.4067/S0718-5839201100...
0.2	-	RiceGrow, China, Tang et al. (2009)TANG, L.; ZHU, Y.; HANNAWAY, D.; MENG, Y.; LIU, L.; CHEN, L.; CAO, W. RiceGrow: a rice growth and productivity model. Wageningen Journal of Life Sciences, v.57, p.83-92, 2009. DOI: https://doi.org/10.1016/j.njas.2009.12.003. https://doi.org/10.1016/j.njas.2009.12.0...
1.2	35	SimulArroz, Brazil, present study
Stem dry matter
2.2	37	Oyza2000, Chile, Artacho et al. (2011)ARTACHO, P.; MEZA, F.; ALCALDE, J.A. Evaluation of the Oryza2000 rice growth model under nitrogen-limited conditions in an irrigated Mediterranean environment. Chilean Journal of Agricultural Research, v.71, p.23-33, 2011. DOI: https://doi.org/10.4067/S0718-58392011000100003. https://doi.org/10.4067/S0718-5839201100...
1.5	68	SimulArroz, Brazil, present study
Panicle dry matter
1.7	72	Oyza2000, Chile, Artacho et al. (2011)ARTACHO, P.; MEZA, F.; ALCALDE, J.A. Evaluation of the Oryza2000 rice growth model under nitrogen-limited conditions in an irrigated Mediterranean environment. Chilean Journal of Agricultural Research, v.71, p.23-33, 2011. DOI: https://doi.org/10.4067/S0718-58392011000100003. https://doi.org/10.4067/S0718-5839201100...
2.5	75	SimulArroz, Brazil, present study
Grain yield
0.6	-	RiceGrow, China, Tang et al. (2009)TANG, L.; ZHU, Y.; HANNAWAY, D.; MENG, Y.; LIU, L.; CHEN, L.; CAO, W. RiceGrow: a rice growth and productivity model. Wageningen Journal of Life Sciences, v.57, p.83-92, 2009. DOI: https://doi.org/10.1016/j.njas.2009.12.003. https://doi.org/10.1016/j.njas.2009.12.0...
1.6	19	Oyza2000, Chile, Artacho et al. (2011)ARTACHO, P.; MEZA, F.; ALCALDE, J.A. Evaluation of the Oryza2000 rice growth model under nitrogen-limited conditions in an irrigated Mediterranean environment. Chilean Journal of Agricultural Research, v.71, p.23-33, 2011. DOI: https://doi.org/10.4067/S0718-58392011000100003. https://doi.org/10.4067/S0718-5839201100...
1.0	7	SimulArroz, Brazil, present study