This study aimed to elucidate the mechanisms underlying the markedly limited dissolution of As in Andosols under reducing conditions, focusing on key factors such as As transformation, iron oxide reduction, and soil affinity for As(III). The dissolution and speciation of As and the generation of Fe(II) in Andosols were compared with those in soils derived from Fluvisols during long-term anaerobic incubation. The affinity for As(III) was evaluated by comparing the solid–solution distribution ratio during the incubation and sorption experiments using reduced soils. The levels of dissolved As remained consistently lower in the Andosols than in other soils throughout the incubation period. Although the decline in Eh and the reduction of As(V) were slower, the final proportion of As(III), which is more soluble than As(V), was similar, except in one Andosol. This was attributed to the fact that a smaller fraction of As(III) was transformed into S-bound As in the Andosols. The increased Fe(II) indicated the dissolution of As-bearing Fe oxides and the loss of As(III) sorption sites; however, residual amorphous Fe oxides persisted at higher levels in the Andosols. The distribution of As(III) between the solution and solids during the incubation and sorption experiments revealed that the Andosols retained a significantly higher affinity for As(III) under reducing conditions. The markedly limited dissolution of As in Andosols under reducing conditions can be attributed primarily to their strong As(III) affinity, which is likely governed by mineralogical characteristics, rather than to slow and limited As(V) reduction.
Optimizing heading date to suit local conditions is key to maximizing yield potential. 'Haruka Nijo', a two-rowed barley (Hordeum vulgare L.) cultivar developed for the Kyushu region of Japan, is early-heading and has superior yield performance compared to the standard cultivar 'Nishinohoshi'. To identify genomic regions associated with early-heading in 'Haruka Nijo', we conducted analysis of quantitative trait loci (QTLs) using recombinant progeny of 'Haruka Nijo' × 'Nishinohoshi' (heading date difference: 2.4-6.0 days). A stable QTL, designated QHD.HN-5H, was detected near the centromere of chromosome 5H. This QTL explained 30.3-46.1% of the phenotypic variance and consistently conferred 2-5 days earlier heading across three seasons. Pedigree analysis indicated that the QHD.HN-5H region in 'Haruka Nijo' likely originated from the Tohoku six-rowed cultivar 'Haganemugi' and was probably co-introduced into Kyushu cultivars together with the Barley yellow mosaic virus resistance gene rym3. Whole-genome sequencing and Gene Ontology analysis identified non-synonymous differences between 'Haruka Nijo' and 'Nishinohoshi' in five heading-related genes within the QTL region. Four of these genes shared identical genotypes between 'Haganemugi' and 'Haruka Nijo', supporting their candidacy. These findings provide new breeding tools to adapt the heading date of barley to the climate and cultivation environment.
Objective The methane (CH4) emission prediction method, using predicted CO2 emissions and the CH4:CO2 concentration ratio, faces challenges in evaluating the efficacy of CH4-reducing feed additives due to CO2 prediction bias associated with energy utilization efficiency. We hypothesized that incorporating dry matter intake (DMI), along with metabolic body weight (MBW) and energy-corrected milk (ECM) as explanatory variables, would reduce this bias. The primary objective was to compare the performance of CO2 emission models with and without including DMI. The secondary objective was to assess the CO2-based method’s applicability for quantifying CH4-reducing effects, through a case study of 3-nitrooxypropanol (3-NOP). Methods Prediction models for CO2 emissions were developed including DMI, MBW, and ECM as explanatory variables, based on 219 records obtained from previous experiments with Holstein cows using respiration chambers or headboxes. The model performance was evaluated using cross-validation. Bias associated with energy utilization efficiency was assessed. The applicability of the CO2-based method to quantify the CH4-reducing effect of 3-NOP was assessed using data obtained from the literature, including 10 studies with 22 treatment and control mean comparisons. The agreement between the observed and predicted CH4 reductions was assessed. Results Combining DMI along with MBW and ECM improved the predictive performance of CO2 emissions. While the models without DMI showed bias associated with energy utilization efficiency, it was eliminated when DMI was incorporated. Applicability assessment demonstrated that the models without DMI systematically underestimated the CH4-reducing effect of 3-NOP. In contrast, the models with DMI showed smaller discrepancies between observed and predicted CH4 reductions. Conclusion This study highlights the importance of incorporating DMI as an explanatory variable to achieve accurate and unbiased predictions of CO2 emissions. These findings would contribute to the appropriate application of the CO2-based method for evaluating the CH4-reducing effects of feed additives.
Spikelet number per unit area is the most important yield component of rice (Oryza sativa L.). While spikelet number in rice is often related to total N content in whole plant, several studies have reported that it is more strongly correlated with dry weight (DW). The aims of this study are to identify the most critical factor determining spikelet number and to evaluate cultivar differences in spikelet production efficiency. In a simple yet unique experiment, we investigated the relationships between spikelet number, DW, and N content of the same plants at heading stage in 27 cultivars. In all cultivars except Tsukisuzuka, with an extremely short panicle, DW at heading stage had a clear proportional relationship with spikelet number. N content had no consistent relationship with spikelet number. The constant of the proportionality between DW and spikelet number estimated from our 2-year dataset at a single site agreed with that estimated from multisite data in previous studies. We concluded that DW at heading was the most critical factor determining spikelet number. Its proportional relationship can be used as a simple model for predicting spikelet number. The proportionality constant reflected cultivar differences in spikelet production efficiency. This study not only settles the question of the most critical factor determining spikelet number, but also efficiently evaluated cultivar differences in spikelet production efficiency, which we expect to contribute significantly to regulating spikelet number in rice.
