Genetic architecture of head rice and rice chalky grain percentages using genome-wide association studies.

High head rice and low chalky grain percentages are key grain quality traits selected in developing rice cultivars. The objectives of this research were to characterize the phenotypic variation of head rice and chalky grain percentages in a diverse collection of rice accessions, identify single nucleotide polymorphism (SNP) markers associated with each of these traits using genome-wide association studies (GWAS), and identify putative candidate genes linked to the SNPs identified by GWAS. Diverse rice varieties, landraces, and breeding lines were grown at the Texas A&M AgriLife Research Center in Beaumont. Head rice percentages (HRP) and chalky grain percentages (CGP) of 195 and 199 non-waxy accessions were estimated in 2018 and 2019, respectively. Phenotypic data were analyzed along with 854,832 SNPs using three statistical models: mixed linear model (MLM), multi-locus mixed model (MLMM), and fixed and random model circulating probability unification (FarmCPU). Significant variations in HRP and CGP were observed between rice accessions. Two significant marker-trait associations (MTAs) were detected on chromosomes 1 and 2, respectively, based on best linear unbiased prediction (BLUP) values in 2018, while in 2019, one SNP was significantly associated with HRP in each of chromosomes 6, 8, 9, and 11, and two in chromosome 7. CGP was significantly associated with five SNPs located in chromosomes 2, 4, 6, and 8 in the 2018 study and ten SNPs in chromosomes 1, 2, 3, 4, 7, 8, 11, and 12 in the 2019 study. The SNPs are located within or linked to putative candidate genes involved in HRP and CGP. This study reports five and ten novel MTAs for HRP and CGP, respectively, while three and five MTAs co-located with previously reported quantitative trait loci for HRP and CGP, respectively. The validation of candidate genes for their roles in determining HRP and CGP is necessary to design functional molecular markers that can be used to effectively develop rice cultivars with desirable grain quality.

Phenotypic variation and genome-wide association studies of main culm panicle node number, maximum node production rate, and degree-days to heading in rice.

BACKGROUND: Grain yield is a complex trait that results from interaction between underlying phenotypic traits and climatic, edaphic, and biotic variables. In rice, main culm panicle node number (MCPNN; the node number on which the panicle is borne) and maximum node production rate (MNPR; the number of leaves that emerge per degree-day > 10°C) are primary phenotypic plant traits that have significant positive direct effects on yield-related traits. Degree-days to heading (DDTH), which has a significant positive effect on grain yield, is influenced by the interaction between MCPNN and MNPR. The objective of this research is to assess the phenotypic variation of MCPNN, MNPR, and DDTH in a panel of diverse rice accessions, determine regions in the rice genome associated with these traits using genome-wide association studies (GWAS), and identify putative candidate genes that control these traits. RESULTS: Considerable variation was observed for the three traits in a 220-genotype diverse rice population. MCPNN ranged from 8.1 to 20.9 nodes in 2018 and from 9.9 to 21.0 nodes in 2019. MNPR ranged from 0.0097 to 0.0214 nodes/degree day > 10°C in 2018 and from 0.0108 to 0.0193 nodes/degree-day > 10°C in 2019. DDTH ranged from 713 to 2,345 degree-days > 10°C in 2018 and from 778 to 2,404 degree-days > 10°C in 2019. Thirteen significant (P < 2.91 x 10-7) trait-single nucleotide polymorphism (SNP) associations were identified using the multilocus mixed linear model for GWAS. Significant associations between MCPNN and three SNPs in chromosome 2 (S02_12032235, S02_11971745, and S02_12030176) were detected with both the 2018 and best linear unbiased prediction (BLUP) datasets. Nine SNPs in chromosome 6 (S06_1970442, S06_2310856, S06_2550351, S06_1968653, S06_2296852, S06_1968680, S06_1968681, S06_1970597, and S06_1970602) were significantly associated with MNPR in the 2019 dataset. One SNP in chromosome 11 (S11_29358169) was significantly associated with the DDTH in the BLUP dataset. CONCLUSIONS: This study identifies SNP markers that are putatively associated with MCPNN, MNPR, and DDTH. Some of these SNPs were located within or near gene models, which identify possible candidate genes involved in these traits. Validation of the putative candidate genes through expression and gene editing analyses are necessary to confirm their roles in regulating MCPNN, MNPR, and DDTH. Identifying the underlying genetic basis for primary phenotypic traits MCPNN and MNPR could lead to the development of fast and efficient approaches for their estimation, such as marker-assisted selection and gene editing, which is essential in increasing breeding efficiency and enhancing grain yield in rice. On the other hand, DDTH is a resultant variable that is highly affected by nitrogen and water management, plant density, and several other factors.

A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Sustainable intensification for a larger global rice bowl.

Future rice systems must produce more grain while minimizing the negative environmental impacts. A key question is how to orient agricultural research & development (R&D) programs at national to global scales to maximize the return on investment. Here we assess yield gap and resource-use efficiency (including water, pesticides, nitrogen, labor, energy, and associated global warming potential) across 32 rice cropping systems covering half of global rice harvested area. We show that achieving high yields and high resource-use efficiencies are not conflicting goals. Most cropping systems have room for increasing yield, resource-use efficiency, or both. In aggregate, current total rice production could be increased by 32%, and excess nitrogen almost eliminated, by focusing on a relatively small number of cropping systems with either large yield gaps or poor resource-use efficiencies. This study provides essential strategic insight on yield gap and resource-use efficiency for prioritizing national and global agricultural R&D investments to ensure adequate rice supply while minimizing negative environmental impact in coming decades.

Hybrid Rice Outperforms Inbred Rice in Resistance to Sheath Blight and Narrow Brown Leaf Spot.

Sheath blight (ShB; caused by Rhizoctonia solani AG1-1A) and narrow brown leaf spot (NBLS; Cercospora janseana) are among the most important diseases affecting rice production in Texas and other southern parts of the United States. Because of the high yield potential, hybrid rice acreage is continually increasing. Understanding the relative levels of resistance to ShB and NBLS in hybrids compared to those of inbreds is important for effective disease management; however, this information remains largely unknown. A comparison of the performance of hybrid rice and inbred rice was performed involving 173 hybrid and 155 inbred genotypes (cultivars and elite breeding lines) over the course of five crop seasons (2016 to 2020) and two locations in Texas. The results showed that genotype, cultivar type (hybrid or inbred), location, and their interactions had significant effects on the severity of ShB and NBLS. The ShB severity in hybrid genotypes was significantly lower than that in inbred genotypes, with an average reduction of 27% in disease severity during the 5-year, two-location evaluation. Most (53%) of the hybrid genotypes were rated moderately resistant, whereas almost all (97%) of the inbred genotypes ranged from very susceptible to moderately susceptible. Similarly, NBLS severity in hybrid genotypes is significantly lower than that in inbred genotypes. All but four hybrid genotypes exhibit immune reactions to NBLS. In contrast, 77% of inbred genotypes exhibit NBLS symptoms, with disease resistance reactions ranging from susceptible to resistant. The results demonstrate that hybrid rice is generally less susceptible to sheath blight and has a higher level of resistance against NBLS compared with inbred rice.

Surfing parameter hyperspaces under climate change scenarios to design future rice ideotypes.

Growing food crops to meet global demand and the search for more sustainable cropping systems are increasing the need for new cultivars in key production areas. This study presents the identification of rice traits putatively producing the largest yield benefits in five areas that markedly differ in terms of environmental conditions in the Philippines, India, China, Japan and Italy. The ecophysiological model WARM and sensitivity analysis techniques were used to evaluate phenotypic traits involved with light interception, photosynthetic efficiency, tolerance to abiotic stressors, resistance to fungal pathogens and grain quality. The analysis involved only model parameters that have a close relationship with phenotypic traits breeders are working on, to increase the in vivo feasibility of selected ideotypes. Current climate and future projections were considered, in the light of the resources required by breeding programs and of the role of weather variables in the identification of promising traits. Results suggest that breeding for traits involved with disease resistance, and tolerance to cold- and heat-induced spikelet sterility could provide benefits similar to those obtained from the improvement of traits involved with canopy structure and photosynthetic efficiency. In contrast, potential benefits deriving from improved grain quality traits are restricted by weather variability and markedly affected by G × E interactions. For this reason, district-specific ideotypes were identified using a new index accounting for both their productivity and feasibility.

A Relative Resistance Ratio for Evaluation of Mexican Rice Borer (Lepidoptera: Crambidae) Susceptibility Among Sugarcane Cultivars.

The Mexican rice borer, Eoreuma loftini (Dyar), is a major pest of sugarcane (hybrids of Saccharum spp.) in Louisiana and Texas. Resistance to E. loftini was evaluated in 51 commercial and experimental cultivars of sugarcane, energycane (hybrids of Saccharum spp.), and sorghum [Sorghum bicolor (L.) Moench and hybrids of Sorghum spp.] in four replicated small plot field experiments from 2009 to 2012. A relative resistance ratio was developed to compare levels of susceptibility among cultivars based on the percentage of bored internodes and survival to adulthood. This index was able to separate cultivars into five resistance categories and provides a new method for comparing levels of resistance among cultivars. E. loftini pest pressure in 2009 was among the highest recorded with injury ranging from 55 to 88% bored internodes. Commercial sugarcane cultivar HoCP 85-845 was identified as resistant in three of four experiments, whereas HoCP 04-838 was identified as susceptible in all experiments. Of the five sugarcane cultivars in commercial production in the Rio Grande Valley of Texas, only TCP 87-3388 was categorized as resistant. Of the cultivars with potential for bioenergy production, all of the energycane cultivars demonstrated higher levels of resistance than high-biomass and sweet sorghum cultivars. Continued evaluation of cultivar resistance to E. loftini is important to development of effective integrated pest management strategies for this pest.

Simulation of biomass yield and soil organic carbon under bioenergy sorghum production.

Developing sustainable management practices including appropriate residue removal and nitrogen (N) fertilization for bioenergy sorghum is critical. However, the effects of residue removal and N fertilization associated with bioenergy sorghum production on soil organic carbon (SOC) are less studied compared to other crops. The objective of our research was to assess the impacts of residue removal and N fertilization on biomass yield and SOC under biomass sorghum production. Field measurements were used to calibrate the DNDC model, then verified the model by comparing simulated results with measured results using the field management practices as agronomic inputs. Both residue removal and N fertilization affected bioenergy sorghum yields in some years. The average measured SOC at 0-50 cm across the treatments and the time-frame ranged from 47.5 to 78.7 Mg C ha-1, while the simulated SOC was from 56.3 to 67.3 Mg C ha-1. The high correlation coefficients (0.65 to 0.99) and low root mean square error (3 to 18) between measured and simulated values indicate the DNDC model accurately simulated the effects of residue removal with N fertilization on bioenergy sorghum production and SOC. The model predictions revealed that there is, in the long term, a trend for higher SOC under bioenergy sorghum production regardless of residue management.

Food security and climate change: on the potential to adapt global crop production by active selection to rising atmospheric carbon dioxide.

Agricultural production is under increasing pressure by global anthropogenic changes, including rising population, diversion of cereals to biofuels, increased protein demands and climatic extremes. Because of the immediate and dynamic nature of these changes, adaptation measures are urgently needed to ensure both the stability and continued increase of the global food supply. Although potential adaption options often consider regional or sectoral variations of existing risk management (e.g. earlier planting dates, choice of crop), there may be a global-centric strategy for increasing productivity. In spite of the recognition that atmospheric carbon dioxide (CO(2)) is an essential plant resource that has increased globally by approximately 25 per cent since 1959, efforts to increase the biological conversion of atmospheric CO(2) to stimulate seed yield through crop selection is not generally recognized as an effective adaptation measure. In this review, we challenge that viewpoint through an assessment of existing studies on CO(2) and intraspecific variability to illustrate the potential biological basis for differential plant response among crop lines and demonstrate that while technical hurdles remain, active selection and breeding for CO(2) responsiveness among cereal varieties may provide one of the simplest and direct strategies for increasing global yields and maintaining food security with anthropogenic change.