ses were not always effective in analyzing the MET data structure. The ANOVA is an additive model that The identification of the highest yielding cultivar for a specific describes main effects effectively and determines if GE environment on the basis of both genotype (G) and genotype ϫ environment (GE) interaction would be useful to breeders and producers since interaction is a significant source of variation, but it yield estimates based only on G and environment (E) effects are does not provide insight into the patterns of genotypes insufficient. The objective of this study was to demonstrate the usefulor environments that give rise to the interaction. The ness of additive main effects and multiplicative interactions (AMMI)PCA is a multiplicative model that contains no sources model analysis and G plus GE interaction (GGE) biplots, obtained

Literature on the path analyses of grain yield and at least 14 yield related traits in a path diagram that is organized with at least second order variables has been lacking. The objectives of this study were to obtain and interpret information on the nature of interrelationships between first‐, second‐, and third‐order yield‐related traits and rice (Oryza sativa L.) grain yield. Fifteen rice genotypes were used in this study to represent the combinations of low and high levels of four traits that were identified as important yield determinants — maximum number of tillers, grain size, panicle node number, and panicle size. ‘Lemont’ and ‘Teqing’ were two of these genotypes. The remaining genotypes were F9 lines from a Lemont × Teqing cross. Field experiments were conducted during the 1994 and 1995 cropping seasons at the Texas A&M University Agricultural Research and Extension Center near Beaumont, TX. The 1994 path coefficient (p) of panicle weight on grain yield (p = 0.72; r2 = 0.93) was used to predict the 1995 grain yield (r2 = 0.90). Based on a path analysis of the combined 1994 and 1995 data, the following traits had positive path coefficients on grain yield: panicle weight (p = 0.84), number of filled grains per panicle (p = 0.67), panicle density (p = 0.52), maximum filler density (p = 0.34), number of spikelets per panicle (p = 0.34), and 100‐grain weight (p = 0.23). The panicle node number has a negative path coefficient on grain yield (p = −0.23). These results may be useful to rice breeders for the indirect selection of grain yield during the early segregating generations when yield tests are not yet being conducted.

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.

A rice (Oryza sativa L.) crop functions as a population of tillers produced at different times and possessing specific growth characteristics. The objective of this study was to characterize the contribution of cultivar tillering ability to dry matter accumulation, yield components, and grain yield. Field experiments were conducted over a 2‐yr period using a completely randomized plot design at Beaumont, TX, grown under a pin‐point flood system, with a China clay soil (fine, smectitic, hyperthermic Oxyaquic Dystrudert). Three cultivars were chosen (Gulfmont, Rosemont, and Teqing), to represent moderate to high tillering abilities, and three plant densities were chosen (56, 112, and 169 plants m−2), to produce differential competition for light and nutrients. Teqing had the highest tillering ability and partitioned more mass to tillers, especially at the lowest plant density, with 77% of the total mass represented by tillers, compared with 71 and 69% for Gulfmont and Rosemont, respectively. However, total dry mass per unit area at harvest was not significantly different, contrasting the plant density treatments (1618, 1725, and 1744 g m−2 for the 56, 112, and 169 plants m−2 treatments, respectively, when averaged across cultivars). Both cultivar and plant density significantly affected a number of yield components, but not kernel weight. The higher yield of Teqing (918 g m−2), contrasted with the lower‐yielding Gulfmont (791 g m−2) and Rosemont (729 g m−2), appears largely to have resulted from its greater tillering ability, higher spikelet density, and longer maturation period, which makes greater use of the relatively long growing season length at Beaumont.

Current knowledge of yield potential and best agronomic management practices for perennial bioenergy grasses is primarily derived from small-scale and short-term studies, yet these studies inform policy at the national scale. In an effort to learn more about how bioenergy grasses perform across multiple locations and years, the U.S. Department of Energy (US DOE)/Sun Grant Initiative Regional Feedstock Partnership was initiated in 2008. The objectives of the Feedstock Partnership were to (1) provide a wide range of information for feedstock selection (species choice) and management practice options for a variety of regions and (2) develop national maps of potential feedstock yield for each of the herbaceous species evaluated. The Feedstock Partnership expands our previous understanding of the bioenergy potential of switchgrass, Miscanthus, sorghum, energycane, and prairie mixtures on Conservation Reserve Program land by conducting long-term, replicated trials of each species at diverse environments in the U.S. Trials were initiated between 2008 and 2010 and completed between 2012 and 2015 depending on species. Field-scale plots were utilized for switchgrass and Conservation Reserve Program trials to use traditional agricultural machinery. This is important as we know that the smaller scale studies often overestimated yield potential of some of these species. Insufficient vegetative propagules of energycane and Miscanthus prohibited farm-scale trials of these species. The Feedstock Partnership studies also confirmed that environmental differences across years and across sites had a large impact on biomass production. Nitrogen application had variable effects across feedstocks, but some nitrogen fertilizer generally had a positive effect. National yield potential maps were developed using PRISM-ELM for each species in the Feedstock Partnership. This manuscript, with the accompanying supplemental data, will be useful in making decisions about feedstock selection as well as agronomic practices across a wide region of the country.