Resources Needed for Record-Breaking Soybean Yields

In 2006, the soybean industry was astounded when Kip Cullers, a farmer in southwest Missouri reported yields of 139 bushels per acre in the Missouri Soybean Association yield contest. These amazing yields were shattered in 2007 when Mr. Cullers reported yields of 155 bushels per acre. Previous to Mr. Cullers\u27 reports, the record yield was 118 bushels per acre set in small-plot research by Dr. Roy Flannery in 1983 at Rutger\u27s University in New jersey

Understanding and increasing soybean yields

An understanding of the main factors influencing grain yield in soybean can provide key insights for making management decisions to increase yield. Seed number is determined by the amount of photosynthate produced between R1 and R5 that is allocated to the seeds, divided by the minimum amount of photosynthate needed to keep a single seed from aborting. Stresses or improvements in crop growth prior to flowering should not have a significant impact on final yield, provided that \u3e95% light interception is achieved by R1. Seed weight is determined by the seed growth rate and the length of the seed fill period. Simplified, soybean yield is mainly determined by photosynthate production from R1 to R5 and the length of the seed fill period. Management practices should focus on maximizing photosynthate production during seed set to increase seed number and limiting stresses during seed fill to extend the seed fill duration and increase seed weight

Bradyrhizobium japonicum and soybean symbiotic response to glyphosate in glyphosate-tolerant soybeans

Soybean (Glycine max) grain contains approximately 40% protein and 6.5% nitrogen (N) on an elemental basis. Therefore, the plant requires an abundant N supply throughout its life cycle, and symbiotic N fixation of soybean with Bradyrhizobium japonicum provides 40 to 85% of the soybean N. Although soybean cultivars have been genetically engineered to withstand the herbicide glyphosate, B. japonicum grown in culture is sensitive to glyphosate. We hypothesized that glyphosate applied to glyphosate-tolerant soybean would inhibit nodulation by B. japonicum unless B. japonicum could also be selected for glyphosate tolerance. Cultures of B. japonicum were challenged with sublethal doses of glyphosate, and individual colonies were selected for growth in the presence of glyphosate. Of the 40 isolates that were originally selected for glyphosate tolerance, all isolates in subsequent experiments had similar sensitivity to glyphosate as wild-type B. japonicum. To determine if glyphosate affected B. japonicum in plants, soybean seeds were imbibed with differing levels of glyphosate and water and then planted and inoculated with B. japonicum. After several weeks of growth the plants were harvested and nodules were scanned and analyzed by digital imagery. Glyphosate application to glyphosate-tolerant soybean did not affect the ability of B. japonicum to form nodules and fix nitrogen. These data do not agree with previous responses of small soybean plants sprayed with glyphosate, which showed delayed nodulation and decreased nodule size. It may be that the dosage applied to plants and the timing of the application affect the response of glyphosate on symbiotic effectiveness

Herbicide resistant dinitrogen fixing bacteria and method of use

The bacteria are useful for effecting N2 fixation, nodulation, growth and yield of herbicide resistant or tolerant leguminous plants treated with herbicide. They are particularly useful for providing competitive advantage to superior N2 fixing rhizobial strains over non-resistant indigenous rhizobia for nodulation of herbicide resistant or tolerant leguminous plants

System and method of determining nitrogen levels from a digital image

A system and method of determining nitrogen levels from a digital image. In particular, a method of determining leaf nitrogen concentration and yield from a digital photograph of a fully developed leaf (collared leaf) of a crop of nonlegumes, such as corn, rice, wheat, cotton, potatoes or sugarcane. The digital image is processed to determine a dark green color index ( DGCI ), which is closely related to leaf nitrogen concentration and yield. Standardized color disks having known DGCI values are included in the digital photograph and serve as an internal standard. The internal standard allows correction of DGCI of samples when using different cameras and/or when lighting conditions change

Effects of measurement methods and growing conditions on phenotypic expression of photosynthesis in seven diverse rice genotypes

IntroductionLight response curves are widely used to quantify phenotypic expression of photosynthesis by measuring a single sample and sequentially altering light intensity within a chamber (sequential method) or by measuring different samples that are each acclimated to a different light level (non-sequential method). Both methods are often conducted in controlled environments to achieve steady-state results, and neither method involves equilibrating the entire plant to the specific light level. MethodsHere, we compare sequential and non-sequential methods in controlled (greenhouse), semi-controlled (plant grown in growth chamber and acclimated to field conditions 2-3 days before measurements), and field environments. We selected seven diverse rice genotypes (five genotypes from the USDA rice minicore collection: 310588, 310723, 311644, 311677, 311795; and 2 additional genotypes: Nagina 22 and Zhe 733) to understand (1) the limitations of different methods, and (2) phenotypic plasticity of photosynthesis in rice grown under different environments. ResultsOur results show that the non-sequential method was time-efficient and captured more variability of field conditions than the sequential method, but the model parameters were generally similar between two methods except the maximum photosynthesis rate (Amax). Amax was significantly lower across all genotypes under greenhouse conditions compared to the growth chamber and field conditions consistent with prior work, but surprisingly the apparent quantum yield (α) and the mitochondrial respiration (Rd) were generally not different among growing environments or measurement methods. DiscussionOur results suggest that field conditions are best suited to quantify phenotypic differences across different genotypes and nonsequential method was better at capturing the variability in photosynthesis

Late-Season Nitrogen Applications Increase Soybean Yield and Seed Protein Concentration

Low seed and meal protein concentration in modern high-yielding soybean [Glycine max L. (Merr.)] cultivars is a major concern but there is limited information on effective cultural practices to address this issue. In the objective of dealing with this problem, this study conducted field experiments in 2019 and 2020 to evaluate the response of seed and meal protein concentrations to the interactive effects of late-season inputs [control, a liquid Bradyrhizobium japonicum inoculation at R3, and 202 kg ha−1 nitrogen (N) fertilizer applied after R5], previous cover crop (fallow or cereal cover crop with residue removed), and short- and full-season maturity group cultivars at three U.S. locations (Fayetteville, Arkansas; Lexington, Kentucky; and St. Paul, Minnesota). The results showed that cover crops had a negative effect on yield in two out of six site-years and decreased seed protein concentration by 8.2 mg g−1 on average in Minnesota. Inoculant applications at R3 did not affect seed protein concentration or yield. The applications of N fertilizer after R5 increased seed protein concentration by 6 to 15 mg g−1, and increased yield in Arkansas by 13% and in Minnesota by 11% relative to the unfertilized control. This study showed that late-season N applications can be an effective cultural practice to increase soybean meal protein concentration in modern high-yielding cultivars above the minimum threshold required by the industry. New research is necessary to investigate sustainable management practices that increase N availability to soybeans late in the season

Allantoate amidohydrolase transcript expression is independent of drought tolerance in soybean

Drought is a limiting factor for N2 fixation in soybean [Glycine max (L.) Merr.] thereby resulting in reduced biomass accumulation and yield. Drought-sensitive genotypes accumulate ureides, a product of N2 fixation, during drought stress; however, drought-tolerant genotypes have lower shoot ureide concentrations, which appear to alleviate drought stress on N2 fixation. A key enzyme involved in ureide breakdown in shoots is allantoate amidohydrolase (AAH). It is hypothesized that AAH gene expression in soybean determines shoot ureide concentrations during water-deficit stress and is responsible for the differential sensitivities of the N2-fixation response to drought among soybean genotypes. The objectives were to examine the relationship between AAH transcript levels and shoot ureide concentration and drought tolerance. Drought-tolerant (Jackson) and drought-sensitive (Williams) genotypes were subjected to three water-availability treatments: well-watered control, moderate water-deficit stress, and severe water-deficit stress. Shoot ureide concentrations were examined, in addition to gene expression of AAH and DREB2, a gene expressed during water-deficit stress. As expected, DREB2 expression was detected only during severe water-deficit stress, and shoot ureide concentrations were greatest in the drought-sensitive genotype relative to the drought-tolerant genotype during water-deficit stress. However, expression of AAH transcripts was similar among water treatments and genotypes, indicating that AAH mRNA was not closely associated with drought tolerance. Ureide concentrations in shoots were weakly associated with AAH mRNA levels. These results indicate that AAH expression is probably not associated with the increased ureide catabolism observed in drought-tolerant genotypes, such as Jackson. Further study of AAH at the post-translational and enzymatic levels is warranted in order to dissect the potential role of this gene in drought tolerance

System and method of in-season nitrogen measurement and fertilization of non-leguminous crops from digital image analysis

Systems and methods of determining nitrogen levels from a digital image and in-season nitrogen measurement and fertilization of non-leguminous crops from digital image analysis are disclosed. In particular, a method of determining leaf nitrogen concentration and yield from a digital photograph of a fully developed leaf (collared leaf) of a crop of non-legumes, such as corn, wheat, rice, cotton, potatoes sugarcane, turfgrass or forage grass species. The digital image is processed to determine a dark green color index ( DGCI ), which is closely related to leaf nitrogen concentration and yield. Standardized color disks having known DGCI values are included in the digital photograph and serve as an comparative standard. The comparative standard allows correction of DGCI of samples when using different cameras and/or when lighting conditions change. The DGCI values can then be used to determine the amount of nitrogen fertilizer that should be applied to recover crop yield potential