Ultraviolet-B Radiation Alters Soybean Growth and Seed Quality

Research on the effects of ultraviolet-B (UV-B) radiation on soybean seed quality is limited. The objective of this study was to quantify UV-B doses, 0, 5, 10 & 15 kJ•m–2•d–1, on soybean growth and seed quality. The experiment was conducted in the Soil-Plant-Atmosphere-Research (SPAR) facility. Chambers located at the R.R. Foil Plant Science Research Facility of Mississippi State University, Mississippi, USA, were used. Each SPAR chamber consists of a steel soil bin to accommodate the root system, a Plexiglas chamber to accommodate plant canopy and a heating, and cooling system connected to air ducts that pass conditioned air to cause leaf flutter through the plant canopy. The SPAR units, supported by an environmental monitoring and control systems, are networked to provide automatic acquisition and storage of the data, monitored every 10 seconds throughout the day and night. Soybean cultivar Pioneer 93Y92 (maturity group IV, Roundup Ready) was used in the study. The desired UV-B radiation was supplied by square-wave UV-B supplementation systems under near ambient PAR and delivered to plants for eight hours, each day, from 08:00 to 16:00 h by eight fluorescent UV-313 lamps. The results showed that increased UV-B did not influence many of the growth parameters because the treatments were imposed at mid-fruiting period. Seed quality parameters that are important for seed industry and human and animal nutrition were all affected by UV-B. Protein and palmitic and oleic acids declined linearly, while oil and linoleic and linolenic acid contents increased with increased UV-B. Sucrose, stachyose, and stearic acid contents showed quadratic trends, increased to about 4 - 5 kJ of UV-B and declined at higher doses. Thus, both current and projected UV-B radiation levels can modify soybean growth and seed quality. The functional algorithms developed in this study could be useful to develop UV-B- specific sub-models for soybean farm management and in policy decision areas.

Reproductive Performance and Fiber Quality Responses of Cotton to Potassium Nutrition

Potassium (K) deficiency affects cotton growth and development and fiber properties. An experiment was conducted in an outdoor pot culture facility by imposing four potassium stress treatments (100%, 40%, 20% and 0% of optimum K level) prior to flowering during 2010 and 2011 growing season. Upland cotton cultivar, TM-1, was seeded in the pots comprised of fine sand as rooting medium. Flowers and bolls were tagged daily to estimate boll maturation period (BMP). Leaf samples were collected every four days from flowering to maturity to estimate leaf K content. Plant height and node numbers were recorded from emergence to 21 days after treatment. Photosynthesis and stomatal conductance were measured weekly from day of treatment imposition to physiological maturity at an interval of seven days. Stem, leaf, and boll dry-component weights, and boll numbers were recorded at the end of the experiment in each year. From each boll, the lint samples were collected and grouped based on average leaf potassium concentration during BMP, and fiber quality parameters were recorded for each group in each treatment. At high K deficient (0 K) condition, total biomass declined by 27% and 28% in years 2010 and 2011, respectively. Significantly, lower numbers of bolls were retained per plant at 0 K stress treatment during both the years. Leaf photosynthesis (r2 = 0.92) and stomatal conductance (r2 = 0.80) declined with declining leaf K levels. Fiber length, strength, micronaire, and uniformity declined linearly with decrease in leaf K content. Weaker fibers with medium length were produced under K-deficient conditions with micronaire values in the discount range. Fiber uniformity, however, did not decline with decrease in leaf K. The identified leaf K status-specific relationships for fiber properties could be used to improve management practices under potassium deficiency and to develop new sub-routines of the existing cotton simulation models. New and improved models will be useful not only in management, but also in arena of policy decisions including future climate change impact assessment analysis.