Yield gap analysis of US rice production systems shows opportunities for improvement

Many assessments of crop yield gaps based on comparisons to actual yields suggest grain yields in highly intensified agricultural systems are at or near the maximum yield attainable. However, these estimates can be biased in situations where yields are below full yield potential. Rice yields in the US continue to increase annually, suggesting that rice yields are not near the potential. In the interest of directing future efforts towards areas where improvement is most easily achieved, we estimated yield potential and yield gaps in US rice production systems, which are amongst the highest yielding rice systems globally. Zones around fourteen reference weather stations were created, and represented 87% of total US rice harvested area. Rice yield potential was estimated over a period of 13–15 years within each zone using the ORYZA(v3) crop model. Yield potential ranged from 11.5 to 14.5 Mg ha−1, while actual yields varied from 7.4 to 9.6 Mg ha−1, or 58–76% of yield potential. Assuming farmers could exploit up to 85% of yield potential, yield gaps ranged from 1.1 to 3.5 Mg ha−1. Yield gaps were smallest in northern California and the western rice area of Texas, and largest in the southern rice area of California, southern Louisiana, and northern Arkansas/southern Missouri. Areas with larger yield gaps exhibited greater annual yield increases over the study period (35.7 kg ha−1 year −1 per Mg yield gap). Adoption of optimum management and hybrid rice varieties over the study period may explain annual yield increases, and may provide a means to further increase production via expanded adoption of current technologies

Genetic Mapping of Quantitative Trait Loci for Grain Yield under Drought in Rice under Controlled Greenhouse Conditions

Drought stress is a constant threat to rice production worldwide. Most modern rice cultivars are sensitive to drought, and the effect is severe at the reproductive stage. Conventional breeding for drought resistant (DR) rice varieties is slow and limited due to the quantitative nature of the DR traits. Identification of genes (QTLs)/markers associated with DR traits is a prerequisite for marker-assisted breeding. Grain yield is the most important trait and to this end drought yield QTLs have been identified under field conditions. The present study reports identification of drought yield QTLs under controlled conditions without confounding effects of other factors prevalent under natural conditions. A linkage map covering 1,781.5 cM with an average resolution of 9.76 cM was constructed using an F2 population from a cross between two Japonica cultivars, Cocodrie (drought sensitive) and Vandana (drought tolerant) with 213 markers distributed over 12 rice chromosomes. A subset of 59 markers (22 genic SSRs and 37 SNPs) derived from the transcriptome of the parents were also placed in the map. Single marker analysis using 187 F2 : 3 progeny identified 6 markers distributed on chromosomes 1, 5, and 8 to be associated with grain yield under drought (GYD). Composite interval mapping identified six genomic regions/quantitative trait loci (QTL) on chromosome 1, 5, 8, and 9 to be associated with GYD. QTLs located on chromosome 1 (qGYD1.2, qGYD1.3), chromosome 5 (qGYD5.1) and chromosome 8 (qGYD8.1) were contributed by Vandana alleles, whereas the QTLs, qGYD1.1 and qQYD9.1 were contributed by Cocodrie alelles. The additive positive phenotypic variance explained by the QTLs ranged from 30.0 to 34.0%. Candidate genes annotation within QTLs suggested the role of transcription factors and genes involved in osmotic potential regulation through catalytic/metabolic pathways in drought tolerance mechanism contributing to yield

Greenhouse Gas Emissions and Management Practices that Affect Emissions in US Rice Systems

Previous reviews have quantified factors affecting greenhouse gas (GHG) emissions from Asian rice (Oryza sativa L.) systems, but not from rice systems typical for the United States, which often vary considerably particularly in practices (i.e., water and carbon management) that affect emissions. Using meta‐analytic and regression approaches, existing data from the United States were examined to quantify GHG emissions and major practices affecting emissions. Due to different production practices, major rice production regions were defined as the mid‐South (Arkansas, Texas, Louisiana, Mississippi, and Missouri) and California, with emissions being evaluated separately. Average growing season CH4 emissions for the mid‐South and California were 194 (95% confidence interval [CI] = 129–260) and 218 kg CH4 ha−1 season−1 (95% CI = 153–284), respectively. Growing season N2O emissions were similar between regions (0.14 kg N2O ha−1 season−1). Ratoon cropping (allowing an additional harvestable crop to grow from stubble after the initial harvest), common along the Gulf Coast of the mid‐South, had average CH4 emissions of 540 kg CH4 ha−1 season−1 (95% CI = 465–614). Water and residue management practices such as alternate wetting and drying, and stand establishment method (water vs. dry seeding), and the amount of residue from the previous crop had the largest effect on growing season CH4 emissions. However, soil texture, sulfate additions, and cultivar selection also affected growing season CH4 emissions. This analysis can be used for the development of tools to estimate and mitigate GHG emissions from US rice systems and other similarly mechanized systems in temperate regions