Evaluating the Performance of Rice Genotypes for Improving Yield and Adaptability Under Direct Seeded Aerobic Cultivation Conditions

Abstract

With the changing climatic conditions and reducing labor-water availability, the potential contribution of aerobic rice varieties and cultivation system to develop a sustainable rice based agri-food system has never been more important than today. Keeping in mind the goal of identifying high-yielding aerobic rice varieties for wider adaptation, a set of aerobic rice breeding lines were developed and evaluated for grain yield, plant height, and days to 50% flowering in 23 experiments conducted across different location in Philippines, India, Bangladesh, Nepal, and Lao-PDR between 2014 and 2017 in both wet and dry seasons. The heritability for grain yield ranged from 0.52 to 0.90. The season-wise two-stage analysis indicated significant genotype x location interaction for yield under aerobic conditions in both wet and dry seasons. The genotype × season × location interaction for yield was non-significant in both seasons indicating that across seasons the genotypes at each location did not show variability in the grain yield performance. Mean grain yield of the studied genotypes across different locations/seasons ranged from 2,085 to 6,433 Kg ha −1 . The best-fit model for yield stability with low AIC value (542.6) was AMMI(1) model. The identified stable genotypes; IR 92521-143-2-2-1, IR 97048-10-1-1-3, IR 91326-7-13-1-1, IR 91326-20-2-1-4, and IR 91328-43-6-2-1 may serve as novel breeding material for varietal development under aerobic system of rice cultivation. High yield and stable performance of promising breeding lines may be due to presence of the earlier identified QTLs including grain yield under drought, grain yield under aerobic conditions, nutrient uptake, anaerobic germination, adaptability under direct seeded conditions, and tolerance to biotic stress resistance such as qDTY 2.1 , qDTY 3.1 , qDTY 12.1 , qNR 5.1 , AG 9.1 , qEVV 9.1 , qRHD 1.1 , qRHD 5.1 , qRHD 8.1 qEMM 1.1 , qGY 6.1 , BPH3, BPH17, GM4, xa4, Xa21, Pita, and Pita2 . The frequency of xa4 gene was highest followed by qAG 9.1 , GM4, qDTY 3.1 , qDTY 2.1 , qGY 6.1 , and qDTY 12.1 .

More and more of less and less: Is genomics‐based breeding of dry direct‐seeded rice (DDSR) varieties the need of hour?

“…Recently, a GWAS on complex mapping population revealed co-location of QTLs for root traits with nal1, OsIPT3 and EL5, OsNAR2.1 genes for root development and nitrate uptake, respectively (Sandhu et al, 2019a). Also, many significant and stable QTLs influencing panicle architecture (Deshmukh et al, 2010;Guo and Hong, 2010;Okada et al, 2018;Sun et al, 2017) and root structure (Arai-Sanoh et al, 2014;Sandhu et al, 2013Sandhu et al, , 2015Sandhu et al, , 2019aUga et al, 2013) are reported and being used for developing DDSRsuited varieties (Sandhu et al, 2019b).…”

“…Root trait QTLs, qNR 4.1 , qNR 5.1 (nodal root number) and qRHD 1.1 , qRHD 5.1 , qRHD 8.1 (root hair density), for higher nutrient availability under dry direct‐seeded conditions, qEVV 9.1 , qEVV 9.2 (early vegetative vigour), qEMM 1.1 (early uniform emergence), qGY 1.1 , qGY 8.1 , qGY 10.1 (grain yield) and qLDG 4.1 (lodging resistance) along with QTLs/genes for biotic resistance ( Gm4, Bph3, Bph17, Pi9, Pita2, Xa4, xa5, xa13, Xa21 ) and major drought QTLs ( qDTY 1.1 , qDTY 2.1 , qDTY 3.1 and qDTY 12.1 ) were pyramided through MAS (Sandhu et al ., 2019b). Single plant selection strategy for grain yield under DDSR condition along with genotyping for targeted QTLs/genes was performed from F 1 to F 6 generations to achieve homozygosity.…”

“…A set of breeding lines generated by complex crosses (possessing DDSR suitable traits) were evaluated for yield performance and adaptability under direct‐seeded aerobic condition, and high‐yielding stable line IR 97041‐8‐1‐1‐1 ( qAG 9.1 / Gm4 / Xa4 / Xa21/Pita / qNR 5.1 ) was identified (Sandhu et al ., 2019b). It showed higher yields of 20.1%, 2.9%, 9.4%, 30.1% and 18.8% over Vandana, BRRI dhan, Hardinath, Tarahara and Sahbhagi dhan in the Philippines, Bangladesh, Nepal and India, respectively.…”

“…The DDSR suitable plant type can be achieved by developing rice varieties through combining traits of upland varieties (tolerance of water deficit, early vigour and weed competitiveness) and lowland varieties (high yield potential, efficient nutrient uptake and good grain quality). The genomic‐breeding efforts to develop varieties possessing traits needed for higher yield potential in DDSR are underway (Sandhu et al ., 2019b). The development of DDSR varieties with ideal plant type could be a sustainable strategy to cope up with water–labour scarcity and climate change.…”

Adaptation of Promising Rice Genotypes for Broadcast Aus Season

“…These two statistical methods (AMMI and GGEBiplot) have wider importance for agricultural researchers because they affect to any two-way data matrices (Farhad et al, 2017;Crossa, 1990). AMMI analysis can also be used to find out the stability of the genotypes across locations using the PCA (principal component axis) scores and AMMI stability value (Sandhu et al, 2019;Purchase, 1997). Research on adaptability of the Aus rice varieties would help farmers to cultivate right varieties in right environments as well as the policy makers to formulate decision in this regard.…”