Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield. In an effort to identify and characterize the elite breeding pool of IRRI’s irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012–2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·ha−1. The rate of genetic gain for grain yield was estimated at 8.75 kg·ha−1 year−1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha−1 year−1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha−1 cycle−1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential. We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity.

Salinity is the second most important abiotic stress after drought that hampers rice production, especially in south and Southeast Asia. Breeding approach supplemented with molecularmarkers-assisted selection is the most promising approach in terms of efficiency to increase the productivity under salt-affected soils. Thirty-day-old rice seedlings of 300 F recombinant-inbred lines derived from a cross between the salt sensitive, IR29 (indica), and a salt tolerant, Hasawi (aus), were used to identify quantitative trait loci (QTLs) linked to salinity tolerance. One hundred and ninety four polymorphic SNP markers were used to construct a genetic linkage map involving 142 selected RILs that covered 1441.96 cM genome with an average distance of 7.88 cMbetween loci. Twenty new QTLs (LOD > 3) were identified on chromosomes 1, 2, 4, 6, 8, 9 and 12 using composite interval mapping with R as high as >20% with LODvalue of 7.21. Many earlier studies reported big qSaltol for seedling stage salinity tolerance in rice is on short arm of chromosome 1 but none of the QTL in our study was on qSaltol or nearby position, therefore, Hasawi conferred salinity tolerance in RILs due to novel QTLs. It is suggested to fine map the novel QTLs so that the level of salinity tolerance could be further enhanced by pyramiding of the different QTLs in one genetic background through marker-assisted selection.

Multiple mycotoxins were tested in milled rice samples (n = 200) from traders at different milling points within the Mwea Irrigation Scheme in Kenya. Traders provided the names of the cultivar, village where paddy was cultivated, sampling locality, miller, and month of paddy harvest between 2018 and 2019. Aflatoxin, citrinin, fumonisin, ochratoxin A, diacetoxyscirpenol, T2, HT2, and sterigmatocystin were analyzed using ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS). Deoxynivalenol was tested using enzyme-linked immunosorbent assay (ELISA). Mycotoxins occurred in ranges and frequencies in the following order: sterigmatocystin (0–7 ppb; 74.5%), aflatoxin (0–993 ppb; 55.5%), citrinin (0–9 ppb; 55.5%), ochratoxin A (0–110 ppb; 30%), fumonisin (0–76 ppb; 26%), diacetoxyscirpenol (0–24 ppb; 20.5%), and combined HT2 + T2 (0–62 ppb; 14.5%), and deoxynivalenol was detected in only one sample at 510 ppb. Overall, low amounts of toxins were observed in rice with a low frequency of samples above the regulatory limits for aflatoxin, 13.5%; ochratoxin A, 6%; and HT2 + T2, 0.5%. The maximum co-contamination was for 3.5% samples with six toxins in different combinations. The rice cultivar, paddy environment, time of harvest, and millers influenced the occurrence of different mycotoxins. There is a need to establish integrated approaches for the mitigation of mycotoxin accumulation in the Kenyan rice.

Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield.In an effort to identify and characterize the elite breeding pool of IRRI’s irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012-2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·ha-1. The rate of genetic gain for grain yield was estimated at 8.75 kg·ha-1·year-1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha-1·year-1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha-1·cycle-1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential.We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity.

Background Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. Results The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia’s and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25–0.88 for plant height, and − 0.29–0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. Conclusion Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.