Varietal improvement options for higher rice productivity in salt affected areas using crop modelling

Abstract

The rice model ORYZA v3 has been recently improved to account for salt stress effect on rice crop growth and yield. This paper details subsequent studies using the improved model to explore opportunities for improving salinity tolerance in rice. The objective was to identify combinations of plant traits influencing rice responses to salinity and to quantify yield gains by improving these traits. The ORYZA v3 model was calibrated and validated with field experimental data collected between 2012 and 2014 in Satkhira, Bangladesh and Infanta, Quezon, Philippines, then used for simulations scenario considering virtual varieties possessing different combinations of crop model parameter values related to crop salinity response and the soil salinity dynamic observed at Satkhira site. Simulation results showed that (i) short duration varieties could escape end of season increase in salinity, while long duration varieties could benefit from an irrigated desalinization period occurring during the later stages of crop growth in the Satkhira situation; (ii) combining short duration growth with salt tolerance (bTR and bPN) above 12 dS m^-1 and a resilience trait (aSalt) of 0.11 in a variety, allows maintenance of 65-70% of rice yield under increasing salinity levels of up to 16 dS m^-1; and (iii) increasing the value of the tolerance parameter b by 1% results in 0.3-0.4% increase in yield. These results are relevant for defining directions to increase rice productivity in saline environments, based on improvements in phenology and quantifiable salt tolerance traits.

Keywords: Cropping systems; Genotype; Modelling; ORYZA v3; Trait selection; Water availability.

Fig. 1

Simulated and observed total above ground (a) and organ storage (b) biomass. Each point represents measured and simulated values from irrigation water treatments in Expts. 1 to 4. The line represents the 1:1 linear relationship between observed and simulated values. FW, control treatment with irrigation with freshwater ; SW, treatment with continuously irrigation with saline water; 1 W, treatment with irrigation with alternate one week fresh and one week saline water in Expts. 1 and 2 and irrigation with a 1:1 mixture of fresh and saline water in Expts. 3 and 4 ; 2 W, treatment with irrigation for two weeks with freshwater and one week with saline water in Expts.1 and 2 and irrigation with a 2:1 mixture of fresh and saline water in Expts. 3 and 4.

Fig. 2

Scenario analysis: simulated grain yield under control conditions (YP) and relative yield (YR) under saline conditions among the phenology groups tested and dates of sowing. Date D1, 1^st December; Date D2, 15^th November. Each bar represents the mean value of simulated YP and calculated RY over 30 years (1984–2014) at Satkhira, Bangladesh. Phenology groups are long duration varieties (P1 to P3 of 110–120 DAS), short duration (P5 and P6), medium duration (P4 and P7).

Fig. 3

Scenario analysis: Changes in relative yield with variation in salinity parameters of tolerance (bTR and bPN) and of resilience (aSalt). The circle and the square symbols represent the relative values of salinity parameters at the relative yield values expected for IR29 and BRRI dhan47, respectively. The lines represent the linear relationship between the change in relative yield (y-axis) and the variation in aSalt (grey line), bPN (dashed line) or bTR (black line) (x-axis). The change in relative yield is compared with the mean relative yield of the reference variety IR64 under the SW treatment. The relative change in the considered traits (increasing from 0 to +1.5 or decreasing from 0 to −1.5) is relative to the reference represented by IR64 parameter values.