Estimation of biomass and canopy height in bermudagrass, alfalfa, and wheat using ultrasonic, laser, and spectral sensors.

Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha(-1), respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha(-1)) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha(-1)). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation.

Genotyping-by-sequencing and genomic selection applications in hexaploid triticale.

Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n = 289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) > 0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r2 > 0.18 (P < 0.05). Genome-wide LD declined rapidly to r2 < 0.1 beyond 10 kb physical distance. The three sub-genomes (A, B, and R) showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement.

Triticale Improvement for Forage and Cover Crop Uses in the Southern Great Plains of the United States.

Triticale (×Triticosecale Wittmack) is a man-made species developed by crossing wheat (Triticum spp.) and rye (Secale cereale L.). It incorporates favorable alleles from both progenitor species (wheat and rye), enabling adaptation to environments that are less favorable for wheat yet providing better biomass yield and forage quality. Triticale has huge potential for both grain and forage production, though research to improve the crop for better adaptation and grain quality is lagging behind that of other small grains. It is also gaining popularity as a cover crop to improve soil health and reduce nutrient leaching. Because of its genetic and flower structure, triticale is suitable for both line and hybrid breeding methods. Advances in the areas of molecular biology and the wealth of genomic resources from both wheat and rye can be exploited for triticale improvement. Gene mapping and genomic selection will facilitate triticale breeding by increasing selection precision and reducing time and cost. The objectives of this review are to summarize current triticale production status, breeding, and genetics research achievements and to highlight gaps for future research.

Rate of Leaf Appearance in Crimson Clover.

Understanding factors that affect growth and development of crimson clover (Trifolium incarnatum L.) are important for the development of management practices to optimize forage utilization. In a 3-yr field experiment at College Station, TX, we evaluated the effects of planting date on rate of leaf appearance of an intermediate- and late-maturing crimson clover. We wanted to determine if growing degree days (GDD) or a photothermal index (PTI) could be used to predict growth. Leaf appearance rates (LAR) did not differ between 'Tibbee' and 'Columbus' crimson clover. Leaf appearance rate was primarily controlled by temperature or GDD, which accounted for 90 to 99% of the variability within each planting date. Photoperiod did not consistently influence the rate of leaf appearance under normal daylengths of 10 h 12 min to 14 h 6 min used in this study. Predictions of LAR were not improved when photoperiod was combined with temperature in a photothermal index than with predictions that used GDD alone. Leaf appearance rate of crimson clover was generally higher when planted in October, November, and December and lower when planted in September, February, and March.

Flowering in Crimson Clover as Affected by Planting Date.

Understanding factors that affect flowering of crimson clover (Trifolium incarnatum L.) could improve management decisions to optimize utilization by improving season of use. The experiment was a split-plot randomized complete block design with three replications at College Station, TX, in the 1997-1998 and 1999-2000 growing seasons, and Overton, TX, in the 1998-1999 growing season. Main plot treatments of two crimson clover cultivars and subplot treatments of six planting dates (PDs) were used to evaluate the effect of date to reach 50% budding and 50% flowering based on day of year (DOY), days after planting (DAP), photothermal index (PTI), and growing degree days (GDD) under field conditions. Correlations with 50% bud and 50% flower were almost identical. 'Columbus' planted in the autumn flowered an average of 49 d later than 'Tibbee'. Date to reach 50% flowering was best correlated with DOY (r = 0.93 and 0.97) and DAP (r = 0.92 and 0.98) for Columbus and Tibbee. Date to reach flowering was not as highly correlated with PTI (r = 0.66 and 0.82) or GDD (r = 0.71 and 0.85) for Columbus and Tibbee, thus temperature could not be used to predict flowering. Planting after 21 December delayed flowering in Tibbee 2 to 9 wks, whereas, Columbus planted after 21 December did not flower. It is important to plant early in the growing season or to use later-maturing cultivars to maximize the length of the growing season and possible total production in grazed environments.