R1 Stage Research Articles

Serine Hydroxymethyltransferase 2 Deficiency in the Hematopoietic System Disrupts Erythropoiesis and Induces Anemia in Murine Models.

Serine and folate metabolism play critical roles in erythroid development in both embryonic and adult mice; however, the precise roles of these metabolic pathways in erythropoiesis and the pathophysiology of anemia remain inadequately characterized in the literature. To delineate the contributions of serine and folate metabolism to erythroid differentiation, we focused on serine hydroxymethyltransferase 2 (SHMT2), a key regulatory enzyme within these metabolic pathways. Using gene-editing techniques, we created fetal and adult mouse models with targeted deletion of Shmt2 in the hematopoietic system. Our findings demonstrated that the deletion of Shmt2 within the hematopoietic system led to the distinctive anemia phenotype in both fetal and adult mice. Detailed progression analysis of anemia revealed that Shmt2 deletion exerts stage-specific effects on the development and maturation of erythroid cells. Specifically, Shmt2 deficiency promoted erythroid differentiation in the R2 (CD71+ Ter119-) cell population residing in the bone marrow while concurrently inhibiting the proliferation and erythroid differentiation of the R3 (CD71+ Ter119+) cell population. This disruption resulted in developmental arrest at the R3 stage, significantly contributing to the anemia phenotype observed in the models. This study elucidates the critical role of Shmt2 in erythroid development within the hematopoietic system, highlighting the underlying mechanisms of erythroid developmental arrest associated with Shmt2 loss.

Insights into management and physiological determinants of lowest pod height in soybean

AbstractHarvest losses caused by the low height of the lowest pods (LPH) are a significant issue in soybean cultivation. Minimizing these losses requires identifying management, physiological, and agronomic factors that interactively modulate LPH. Four studies were conducted to examine the relationships among soybean LPH, node and internode features, and light quality under different management practices. These practices included population density (19, 31, and 43 plants m−2) and row width (equidistant, 25, 51, and 76 cm), relative maturity (maturity group [MG] 0.8, MG 2.1, and MG 2.8), mulch color (gray bare soil, red mulch, and white mulch), and timing of stand reduction (V1, R3, R4, and R5 growth stages). An increase in population density from 19 to 43 plants m−2 led to an average increase in LPH of 28%, from 11.9 to 15.3 cm. LPH was not influenced by row width. Later maturing cultivars demonstrated the highest potential for enhancing LPH, with late AG2802 having a higher LPH (18.8 cm) than early AG0803 (12.4 cm). Data indicated that the elongation of internodes 10, 11, and 12, along with changes in the red to far‐red light ratio beneath the canopy, plays a pivotal role in determining the location of the lowest pods. Moreover, LPH is established around the R3 growth stage. Nevertheless, further investigations are warranted to gain a better understanding of how these parameters, individually and collectively, influence LPH in soybean.

Unveiling the impact of organically activated biochar on physiological, biochemical, and yield attributes of maize under varied field moisture conditions.

Water deficiency in semiarid regions is a limiting factor that affects crop quality and yield. In Punjab, Pakistan, a 27% decline in maize yield was detected over the past two decades just because of water scarcity. Currently, no studies have reported the effects of organically activated biochar (AB) on crop productivity under natural field conditions. For this purpose, a field experiment in a split-split-plot design was conducted with three amendment levels (0, 2, and 4 tonnes ac-1), and three maize hybrids (DK-9108, DK-6321, and Sarhaab) under 100%, 70%, and 50% irrigation water (IW) of crop evapotranspiration (ETc). The AB significantly improved the soil's physical and chemical properties, and maximum improvement was recorded in 4 tonnes ac-1 AB amendment in organic matter (16.6%), total organic carbon (17%), phosphorus (11.43%), and available potassium (29.27%). The 4 tonnes ac-1AB amendment in soil had a significant impact on total chlorophyll content (0.3-1-fold in DK-6321), carotenoid content (3.9-4.4-fold in Sarhaab), and relative water content (30% and 21% in Sarhaab) under 50% irrigation water (IW) of ETc at V14th and R3 stages, respectively. Moreover, a significant decline in stress markers (proline content and sugar content) was detected at both growth stages in all maize hybrids in AB amended soil. The analysis of plant metabolites indicated increased intensities of phenolics, alkyl esters, and carbohydrates by 2.5-7%, 17-80%, and 40-43% in DK-6321 under 50% IW in 2-4 tonnes ac-1 AB amended soil, respectively. The highest improvement in growth and yield attributes among maize hybrids was detected in the order DK-6321¿DK-9108¿Sarhaab in 2-4 tonnes ac-1AB amended soil under 70% and 50% IW of ETc, respectively. Hence, this research might help to develop an effective soil amendment to restore degraded soils and improve maize growth under arid climatic conditions.

Identifying the Numerical Components Affecting Soybean (Glycine max) Yield Under Waterlogging at Reproductive Stages

ABSTRACTWaterlogging is a critical abiotic stress increasing in importance due to more intense, erratic rainfall associated with climate change. Waterlogging leads to significant yield losses in sensitive crops, such as soybean (Glycine max [L.] Merr.). Identifying soybean genotypes and traits associated with better waterlogging tolerance is of high interest. We assessed the response of six soybean genotypes, selected from a field screening of over 190 genotypes, to 10 days of waterlogging at the R1 (onset of flowering) and R4 (grain filling) stages. We evaluated yield and its components, as well as shoot and root dry weights (DW) at the end of the waterlogging treatments and at maturity, along with morphological traits such as plant branch number, stem diameter and plant height. By integrating all these traits, a waterlogging tolerance index (WTI) was calculated for each genotype to rank their sensitivity. The WTI showed variations among genotypes from 0.61 to 0.77, indicating genotypic variation in response to waterlogging. Greater reductions in root DW compared to shoot DW were observed immediately after waterlogging. By maturity, shoot DW of waterlogged plants was more severely reduced than root DW in all genotypes. Despite similar DW losses at R1 and R4 at physiological maturity, seed number per plant and 100‐seed weight responses differed between the treatments. Genotypes that performed well under control conditions suffered significant yield reductions of 70%–85% after waterlogging, mainly due to fewer fertile nodes and seeds per pod, with some also experiencing a notable decrease in 100‐seed weight. In contrast, other genotypes had milder responses, with less severe reductions in seed and pod traits. Identifying breeding soybean genotypes tolerant to waterlogging during reproductive stages that maintain the number of fertile nodes and pods per node without changes in seeds per pod could significantly mitigate yield losses from waterlogging.

First Report of Stem Blight of Mungbean (Vigna radiata) Caused by Diaporthe longicolla in the United States.

Mungbean (Vigna radiata) is primarily grown in Asia and directly consumed by humans. U.S. consumers embraced mungbean as a plant-based protein in vegan eggs and meat substitutes. New cultivars are being developed for American farmers because of the crop's tolerance to heat and drought, and its adaptability to current farming infrastructure. Mungbean's short season complements various cropping systems such as intercropping, alternative cropping, and green manure. With rotations and inclusion with soybean systems, there is a concern about the overlap of common pathogens for soybean and mungbean. During August 2022 when mungbeans reached full maturity (growth stage R6), reddish-brown and necrotic stem lesions with linear rows of black pycnidia were observed on Berken and OK2000 cultivars at fields located in Hancock County, IA and Story County, IA in the United States. Pycnidia measured 0.5-0.6mm in length. Disease incidence was approximately 10% of plants in Hancock County, IA and less than 3% of plants in Story County, IA. Pycnidia from 16 plants were excised and immersed in a 0.5% NaOCl solution for 1 min, rinsed with autoclaved distilled water, and placed onto potato dextrose agar (PDA). Eighteen isolates were hyphal tipped and grown on PDA and were stored at 25°C. Isolates were then visually identified by culture and conidia morphology (Hobbs et al. 1985, Santos et al. 2011). Colonies were cream to white, dense, and floccose. Large black stromata were formed in a concentric pattern or scattered; alpha conidia were ellipsoidal. Template DNA for PCR amplification of the internal transcribed spacer region of the nuclear ribosomal DNA operon (ITS) and the beta-tubulin gene (TUB) was extracted from 18 isolates by scraping mycelia with a sterile pipette tip and transferring it into 50 ul of PrepMan Ultra Sample Reagent (Applied Biosystems, Foster City, California, USA). Fungal primers were ITS1 and ITS4 (White et al. 1990) and Bt-2F/Bt-2R (Udayanga et al. 2014). Sequences of isolates obtained from fields in both counties were identical, providing no species diversity. GenBank accession numbers for the ITS region were PP105598 and PP105599; PP108254 and PP108255 for TUB sequences. BLAST results showed the ITS 550/550 base pairs with type specimen D. longicolla ATCC 60326 GB NR_144924 and the TUB 446/446 base pairs with type specimen D. longicolla ATCC 60325 GB KJ610883. Thus, the isolates were identified as Diaporthe longicolla (Hobbs) J.M. Santos, Vrandecic & A.J.L. Phillips based on morphology and molecular characters (Santo et al 2011; Udayanga et al. 2014). To confirm the pathogenicity of the D. longicolla isolates, twenty mungbean plants (cv. Berken and OK2000) were grown in the greenhouse at 85% RH and 16hr light for 20 days. Inoculum was prepared by placing sterile toothpicks on 1/3 PDA with a single representative isolate from each field location for 21 days (Ghimire et al. 2019). Mungbean plants were grown in a 10cm-by-10cm pot containing a greenhouse professional growing mix (Sungrow, Agawam, Massachusetts, USA) and grown for 30 days post emergence. After 12 days of growing, a 3mm segment of the infested toothpick was inserted into a stem wound below the first trifoliate and sealed with parafilm. A sterile toothpick was inserted into the control plants. After 14 days, red lesions extended downward 1 to 3 cm from the inoculation site, and white mycelial was present in the wound. At 21 days red lesions spanned 3 to 9 cm upward and downward from the inoculation site. Pycnidia were present on collapsed stem tissue, and leaves became chlorotic. Damage was limited to 2mm from the mock-inoculation site, with no discoloration in the control plants. Symptomatic tissues were plated and compared to the original isolates. Alpha conidia were ellipsoidal with the base end rounded. To our knowledge, this is the first report of Diaporthe longicolla causing disease on mungbean within the U.S. and worldwide. The presence of this disease in two locations suggests the potential for Diaporthe longicolla to be a serious disease of mungbean in the future.

Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model.

Importance of brassinosteroid in soybean yield

The aim of the present study was identify the effects of brassinolide plant regulator in soybean plants yield. The work was carried out at Olhos d’água farm located in Ipameri, Goiás, Brazil. It was used the Pioneer TEC 7022 IPRO with six brassinolide concentrations (0, 0.025, 0.050, 0.075, 0.1 and 0.125 ml L-1) of a pre-prepared solution and pulverized with a 100 L ha-1 flow rate. The applications were executed in R3 and R6 stages. The experiment was carried out following the completely randomized design with five replications. Each experimental plot was set up with 18x222 meters, with 0.5 meters between lines and 11 plants per linear meter. The brassinolide applications in R3 and R6 stages with 0.072 ml L-1 concentration increased the yield in 25.1%. Brassinolide increase the grains sink-source relationship and change the assimilates partition to reproductive development (pods number, grain number, grain weight per plant and yield) instead of vegetative growth, without compromising absorption of soil solution.

Responses of Leaf Expansion, Plant Transpiration and Leaf Senescence of Different Soybean (Glycine max. (L.) Merr.) Genotypes to Soil Water Deficit

ABSTRACTThe responses of eco‐physiological processes such as leaf expansion, plant transpiration and senescence to soil water deficit have been reported to be genotype‐dependent in different crops. To study such responses in soybean (Glycine max. (L.) Merr.), a 2‐year (2017 and 2021) outdoor pot experiment was carried out on the Heliaphen automated phenotyping platform at INRAE in Toulouse (France). Six soybean cultivars (Sultana‐MG 000, ES Pallador‐MG I, Isidor‐MG I, Santana‐MG I/II, Blancas‐MG II and Ecudor‐MG II) belonging to four maturity groups (MG) commonly grown in Europe were subjected to progressive soil water deficit from the reproductive stage R1 for 17 and 23 days in 2017 and 2021, respectively. The fraction of transpirable soil water (FTSW) was used as an indicator of soil water deficit. Non‐linear regression was used to calculate FTSWt, that is, the FTSW threshold for which the rate of the eco‐physiological process in stressed plants starts to diverge from a reference value. According to FTSWt, the three eco‐physiological processes showed significant differences in sensitivity to water deficit: leaf expansion exhibits the highest sensitivity and the widest range (FTSWt: 0.44–0.93), followed by plant transpiration (FTSWt: 0.17–0.56), with leaf senescence showing the narrowest range (FTSWt: 0.05–0.16). Among six cultivars, regarding leaf expansion, Cvs Santana (FTSWt = 0.48 in 2017; FTSWt = 0.44 in 2021), Blancas (FTSWt = 0.51 in 2017; FTSWt = 0.48 in 2021) and Ecudor (FTSWt = 0.46 in 2017; FTSWt = 0.52 in 2021) in late MGs (I/II to II) exhibited higher tolerance to soil drying. Conversely, the cv. Sultana in the earliest MG (000) showed the highest sensitivity (FTSWt = 0.91 in 2017; FTSWt = 0.93 in 2021) to water deficit. However, concerning the FTSWt values for plant transpiration (0.17–0.56 in 2017; 0.19–0.31 in 2021) and senescence (0.05–0.16 in 2017; 0.06–0.16 in 2021), their range did not demonstrate a correlated trend with the MG. In addition, a negative linear correlation was observed between values of FTSWt of normalised leaf expansion at the whole‐plant level (NLE) and specific leaf area (SLA) measured on irrigated plants for both years. This suggests that genotypes with high values of SLA could be associated with higher tolerance of leaf expansion to soil water deficit. Such a non‐destructive phenotyping method under outdoor conditions could bring new information to variety testing process and provide paths for integrating genotypic variability into crop growth models used for simulating soybean eco‐physiological responses to water deficit across the plant, field and even regional scales.

Factors influencing fumonisin B1 contamination in maize: insights from two production regions in Costa Rica.

Maize (Zea mays L.) is an important cereal crop worldwide. Contaminated maize kernels pose a significant mycotoxin exposure risk for humans in Latin America. Fumonisins, the most prevalent mycotoxin in maize, typically occur during pre-harvest conditions leading to significant economic losses. Various factors, including weather conditions, may influence this contamination. This study aimed to determine the association between fumonisin B1 (FB1) contamination, prevalence of Fusarium verticillioides, weather conditions and kernel quality in the two primary maize production areas in Costa Rica (Brunca and Chorotega). All maize samples (100%) showed FB1 contamination, with higher concentrations in samples from Brunca region, consistent with the presence of F. verticilliodes. Weather conditions appeared to play an important role in this contamination, since Brunca region had the highest mean temperature and relative humidity after maize silking (R1) and the total monthly rainfall in this region was significantly higher during the last two months of maize cultivation (grain-filling and physiological maturity stages R3 to R6). Interestingly, this study found a negative correlation between grain damage and kernel contamination with FB1 and F. verticillioides. The concentration of mineral nutrients in kernels from both regions was largely similar. Most nutrients in kernels exhibited a negative correlation with FB1, particularly nitrogen. Zinc and phosphorus were the only nutrients in kernels showing a positive correlation with FB1 in samples from the Brunca region. The results highlight elevated levels of FB1 contamination in maize and contribute to a better understanding of pre-harvest factors influencing FB1 contamination in tropical conditions.

Screening New Mungbean Varieties for Terminal Drought Tolerance

Rainfed mungbean crops in Queensland Australia frequently experience terminal drought (drought stress in the final stages of reproductive development), highlighting the importance of drought-tolerant varieties for sustainable mungbean production. Given there is limited information on the relative drought tolerance of current mungbean varieties in Australia, the study of genetic variations and mechanisms of drought tolerance in summer mungbean can provide a basis for developing drought-tolerant mungbean varieties. This study evaluated the physiological, biochemical, and phenological traits underpinning yield attributes associated with drought tolerance in selected mungbean varieties. Four new mungbean varieties (AVTMB#1 to 4) and the Australian commercial line (Jade-AU) were grown in tall (75 cm) polyvinyl chloride (PVC) lysimeters where drought stress was imposed at the early flowering stage (R1) and maintained until maturity. Drought stress significantly impacted all the varieties. Averaged across all the varieties, drought stress was associated with a reduction in stomatal conductance (gs) and photosynthetic rate (Asat) by 78% and 86%, respectively, compared to well-watered plants. Internal carbon dioxide concentration (Ci), the effective quantum yield of photosystem II (ΦPSII) and maximum light-use efficiency of light-acclimated photosystem II (PSII) centres (Fv’/Fm’) were also decreased, while excitation pressure (1-qP) increased with drought treatment. A positive correlation (r = 0.60) existed between seed yield and ΦPSII assessed at R1, while a weak correlation with Fv’/Fm’ (r = 0.24) was observed. Excitation pressure (1-qP) at the R1 stage was negatively correlated with seed yield (r = −0.66). Therefore, leaf fluorescence measures, viz., 1-qP and ΦPSII, were recommended for use in screening mungbean varieties for drought tolerance. The varieties, AVTMB#1 and AVTMB#4, respectively achieved 39 and 38% greater seed yields relative to the commercial variety, Jade-AU, under terminal drought conditions.

Enhancement of Waterlogging Tolerance and Improvement of Grain Quality in Waxy Maize With Exogenous EDAH: A Mixture of Ethephon and Diethyl Aminoethyl Hexanoate

ABSTRACTGlobal warming has led to more frequent extreme weather events, such as heavy summer rains, in the Huang‐Huai‐Hai region. These events significantly impede the growth and development of waxy maize in the area and disrupt the stable progression of the industry. However, there is a lack of effective agricultural measures to mitigate the impact of waterlogging, and the underlying regulation mechanisms remain unclear. To fill this knowledge gap, we conducted a two‐year experiment to assess whether exogenous EDAH (a mixture of ethephon and diethyl aminoethyl hexanoate (DA‐6), ethephon: DA‐6 = 27%: 3%) application during the waxy maize V6 stage, combined with 10 days of waterlogging treatment at the V6, VT and R2 growth stages. The results indicate that exogenous EDAH mitigates the adverse effects of waterlogging stress to a certain extent. It is noteworthy that exogenous EDAH increases the leaf area index and photosynthetic parameters of waxy maize, enhances the activity of catalase in ear leaves at the R3 stage, inhibits the accumulation of malondialdehyde and delays premature aging of plants. Furthermore, exogenous EDAH delays premature ripening of grains caused by waterlogging, increases the moisture content of fresh waxy maize grains during the fresh edible period, but does not effectively mitigate the yield losses caused by waterlogging. However, exogenous EDAH effectively improves grain quality under waterlogging stress, increasing the soluble sugar content and total protein content while reducing starch content, ultimately enhancing the edibility of fresh ears. Through TOPSIS comprehensive evaluation, it can be inferred that exogenous EDAH effectively mitigates the overall impact of waterlogging on waxy maize at both the V6 and VT stages. This research sheds light on potential strategies to mitigate the adverse effects of waterlogging on agricultural productivity and grain quality.

Comparative proteomics in tall fescue to reveal underlying mechanisms for improving Photosystem II thermotolerance during heat stress memory

BackgroundThe escalating impacts of global warming intensify the detrimental effects of heat stress on crop growth and yield. Among the earliest and most vulnerable sites of damage is Photosystem II (PSII). Plants exposed to recurring high temperatures develop heat stress memory, a phenomenon that enables them to retain information from previous stress events to better cope with subsequent one. Understanding the components and regulatory networks associated with heat stress memory is crucial for the development of heat-resistant crops.ResultsPhysiological assays revealed that heat priming (HP) enabled tall fescue to possess higher Photosystem II photochemical activity when subjected to trigger stress. To investigate the underlying mechanisms of heat stress memory, we performed comparative proteomic analyses on tall fescue leaves at S0 (control), R4 (primed), and S5 (triggering), using an integrated approach of Tandem Mass Tag (TMT) labeling and Liquid Chromatography-Mass Spectrometry. A total of 3,851 proteins were detected, with quantitative information available for 3,835 proteins. Among these, we identified 1,423 differentially abundant proteins (DAPs), including 526 proteins that were classified as Heat Stress Memory Proteins (HSMPs). GO and KEGG enrichment analyses revealed that the HSMPs were primarily associated with the “autophagy” in R4 and with “PSII repair”, “HSP binding”, and “peptidase activity” in S5. Notably, we identified 7 chloroplast-localized HSMPs (HSP21, DJC77, EGY3, LHCA4, LQY1, PSBR and DEGP8, R4/S0 > 1.2, S5/S0 > 1.2), which were considered to be effectors linked to PSII heat stress memory, predominantly in cluster 4. Protein-protein interaction (PPI) analysis indicated that the ubiquitin-proteasome system, with key nodes at UPL3, RAD23b, and UCH3, might play a role in the selective retention of memory effectors in the R4 stage. Furthermore, we conducted RT-qPCR validation on 12 genes, and the results showed that in comparison to the S5 stage, the R4 stage exhibited reduced consistency between transcript and protein levels, providing additional evidence for post-transcriptional regulation in R4.ConclusionsThese findings provide valuable insights into the establishment of heat stress memory under recurring high-temperature episodes and offer a conceptual framework for breeding thermotolerant crops with improved PSII functionality.

Relationship between dry matter accumulation and maize yield in Southwest China

AbstractTo explore the differences in dry matter accumulation and yield of maize varieties having different nitrogen‐use efficiencies in Southwest China, a field experiment was conducted in Yongchuan, Chongqing, and Deyang, Sichuan, from 2019 to 2020. Two varieties, the nitrogen‐efficient Zhenghong 311 (ZH 311) and the nitrogen‐inefficient Xianyu 508 (XY 508), were tested across four nitrogen levels (0–360 kg ha−1). The results showed that compared to XY 508, ZH 311 exhibited a significantly higher accumulation of dry matter at various stages and periods, particularly in the roots during the R6 stage, and in the stem sheaths and leaves throughout all stages. Furthermore, the number of kernel rows, number of kernels per row, number of kernels per ear, and grain yield were significantly higher for ZH 311 than XY 508, whereas the 100‐grain weight was significantly lower for ZH 311 than XY 508. The yield difference between the two varieties was the largest when the nitrogen application rate was 240 kg ha−1. The yield performance of ZH 311 was always better than that of XY 508, and less nitrogen was needed to obtain the best yield. The accumulation of maize dry matter had a highly significant effect on the number of kernel rows, kernels per row, and kernels per ear, and grain yield. The direct effect of the number of kernels per ear on grain yield was very low. However, it affected grain yield through the number of kernel rows and kernels per row. The dry matter accumulation of V6−V12 and R3−R6 contributed the most to grain yield, while in vegetative organs, the effect of leaf dry matter accumulation and yield was the greatest. This investigation will provide insights into factors affecting variations in maize yield under low nitrogen conditions and offer guidance for N‐fertilizer management strategies.

Defoliation level and timing affect popcorn yield traits in the Midwestern United States

Popcorn [Zea mays (L.) var. Everta] is an important field crop in the Midwestern United States. However, hailstorms are a major threat to ensuring high yield because of their effects on leaf photosynthesis, pollination, and stalk lodging. A multi-state study spanning six site-years (2020–2023) was conducted in two locations (Ohio and Nebraska) to determine how defoliation intensity at different stages affects yield loss in popcorn and compare these losses with current values in the National Crop Insurance Service (NCIS) Corn Loss Instructions. At each location the study was conducted in a randomized complete block design with four replicates. The treatments consisted of the combination of four levels of leaf loss (25, 50, 75 and 100 % leaf blade area removal) imposed at five developmental stages: V8 (10-leaf), V13 (15-leaf), VT, R2, and R4. The evaluated traits were plant population, grain yield, stalk lodging, and 100-kernel weight. All evaluated traits were affected by defoliation, except plant population. The treatments 100 % defoliation at VT and R2 stages were the most damaging in terms of grain yield reduction, number of ears per plant and 100-kernel weight for both locations. In Ohio, the greatest stalk lodging was observed at 100 %-R2 and in Nebraska, at 75 %-VT. For both locations, the actual yield losses in the defoliated treatments were lower than values currently listed in the NCIS Popcorn Leaf Loss Chart. These results indicate leaf loss chart values should be updated to accurately reflect yield losses from popcorn defoliation events. This evaluation did not impose damage to stalks (e.g., bruising) or to plant whorls as may be expected in natural events. Future research should incorporate other forms of plant damage such as those described to provide a better emulation of hail damage.

Plant Growth Promoting Rhizobacteria (PGPR): Impact on peanut flowering, seed physical quality, and yield determination (Arachis hypogaea L.)

Peanut seed yield is influenced by physiological and ecophysiological crop traits, with changes in reproductive parameters such as flowering time, flower number, and fertility index significantly affecting productivity. Management practices like Plant Growth-Promoting Rhizobacteria (PGPR) could enhance crop yield. PGPR can improve flowering and yield by modulating the balance of phytohormones and enhancing reproductive processes. This study assessed PGPR effects on peanut flowering dynamics, seed yield determination, and correlation with hormone levels. Two plant-level field experiments were conducted during 2019–2020 and 2020–2021, sowing two cultivars (ASEM 400 INTA and Granoleico) on four sowing dates (Oct-05, Oct-15, Nov-29, Dec-10). Three PGPR strains (Bacillus velezensis RI3 and SC6, Pseudomonas psychrophila P10), Bradyrhizobium japonicum, and control were arranged in a split-split-split-plot design, with sowing dates as main plots, cultivars as sub-plots, and PGPRs as sub-sub-plots. PGPR improved peanut flower number (FN) and flowering rate (FR) by 20 % and 50 % compared to B. japonicum and the control. B. velezensis SC6 showed the best performance (SC6 > P10 > RI3), advancing the flowering peak by 4–12 days. PGPR also increased the fertility index (FI) to 0.31 vs. 0.24 of B. japonicum and control and pod number (PN) by 53 % compared to the control. Flowering peak advancement led to larger seeds (SF≥8 mm) (∼+8 % vs. control) due to better environmental conditions (i.e., higher temperatures). Seed number (SN) increased by 94 %–113 % under PGPR treatments. Seed yield (SY) was increased (∼75 % on average) due to higher total biomass (TB) production and improved carbon allocation to seeds (harvest index; 0.33–0.36). Hormone analysis at the R5 stage under late sowing's restrictive conditions showed hormone level increases of 15 %–90 %. This study demonstrates that PGPR significantly improves peanut flowering dynamics, seed quality, and yield by affecting hormone levels and reproductive parameters, highlighting its potential as a sustainable management practice.

Maize and legume intercropping enhanced crop growth and soil carbon and nutrient cycling through regulating soil enzyme activities

Maize intercropped with leguminous green manure (LGM) has been proven as a sustainable plantation practice for enhancing crop yield and soil fertility. However, a comprehensive understanding of how intercropped legumes coordinate the above- and below-ground performance during maize growth under low to high N application remains elusive. This study aimed to investigate the effects of biological C and N input on soil fertility and maize growth by regulating soil enzyme activities in maize/LGM intercropping systems. A three-year field trial was conducted in northwestern China, where maize was intercropped with two LGMs, namely common vetch and pea, under no N (N0, 0 kg ha−1) and conventional N (N330, 330 kg ha−1) applications. The grain yield of intercropped maize ranged from 10.54 to 11.08 t ha−1, representing a significant increase of 6.6–12.1 % compared to monoculture maize in the N0 treatment. Nitrogen application substantially increased maize grain yield by 39.2–52.0 % across cropping systems relative to the N0 treatments, while minor differences were observed in maize yield between cropping systems in the N330 treatments. Compared with monoculture maize, intercropped with LGMs increased C input by 16.7–79.2 % at maize V9 stage and 81.1–140.3 % at the R6 stage. The biological N fixation of intercropped LGMs was 62.7–89.5 kg ha−1 and 8.0–12.8 kg ha−1 in the N0 and N330 treatments, respectively. Biological C and N input greatly facilitated soil enzyme activities and the associated nutrient cycling, consequently improving soil fertility. Soil organic matter and total N in the intercropping systems significantly increased by 4.4–14.3 % relative to monoculture across maize growth stages, irrespective of N levels. Furthermore, increased soil fertility was closely associated with nutrient stoichiometry, which in turn facilitated maize nutrient uptake and shoot recovery growth in the intercropping systems. Overall, these findings highlight that increased biological C and N input to belowground enhances soil C and nutrient cycling by regulating the involved enzyme activities, which in turn improves maize nutrient uptake and growth, forming a coordinated loop of C and nutrient flow in the maize and legume intercropping systems. Maize intercropped with LGMs is a sustainable practice that improves soil fertility and promotes maize growth by augmenting biological C and N input.

Chemical constraints are the major limiting factor of root deepening in southern Brazil soils

Large soybean yield gaps are related to irregular rainfall patterns in several rainfed soybean crop regions, such as southern Brazil. More efficient rainwater use can be achieved in these regions through deeper roots. Therefore, assessing the status of soybean rooting and soil-related factors that drive root growth in soybean fields is mandatory for making management decisions that minimize the yield gap. To this end, we conducted a survey in 10 fields in the state of Rio Grande do Sul (RS), Brazil, during the 2021/22 season. Undisturbed soil blocks and cores and disturbed soil samples were collected in trenches up to approximately 80 cm deep when the soybean was at the R5 stage. The roots washed from the soil blocks were scanned to determine the relative root length density (RRLD), whereas the soil cores and disturbed samples were used for physical and chemical analyses. Hierarchical cluster analysis based on the RRLD was performed to group fields according to their similarities in root distribution and soil-related factors. Soybean rooting in RS fields is quite shallow, with >72% of the total RRLD in all fields located within the 0–20 cm layer and <15% deeper than 40 cm in most fields. Chemical constraints, such as high gradient of acidity-related variables along soil profile and low concentration of nutrients below 20 cm depth, limited soybean root deepening in 50% of the fields, while physical constraints were predominant in 30% of the fields. Thus, management strategies for reducing soybean yield gap by increasing rooting depth in fields of RS need to focus on improving subsurface chemical conditions for root growth.

Estimation of soybean yield based on high-throughput phenotyping and machine learning.

Soybeans are an important crop used for food, oil, and feed. However, China's soybean self-sufficiency is highly inadequate, with an annual import volume exceeding 80%. RGB cameras serve as powerful tools for estimating crop yield, and machine learning is a practical method based on various features, providing improved yield predictions. However, selecting different input parameters and models, specifically optimal features and model effects, significantly influences soybean yield prediction. This study used an RGB camera to capture soybean canopy images from both the side and top perspectives during the R6 stage (pod filling stage) for 240 soybean varieties (a natural population formed by four provinces in China: Sichuan, Yunnan, Chongqing, and Guizhou). From these images, the morphological, color, and textural features of the soybeans were extracted. Subsequently, feature selection was performed on the image parameters using a Pearson correlation coefficient threshold ≥0.5. Five machine learning methods, namely, CatBoost, LightGBM, RF, GBDT, and MLP, were employed to establish soybean yield estimation models based on the individual and combined image parameters from the two perspectives extracted from RGB images. (1) GBDT is the optimal model for predicting soybean yield, with a test set R2 value of 0.82, an RMSE of 1.99 g/plant, and an MAE of 3.12%. (2) The fusion of multiangle and multitype indicators is conducive to improving soybean yield prediction accuracy. Therefore, this combination of parameters extracted from RGB images via machine learning has great potential for estimating soybean yield, providing a theoretical basis and technical support for accelerating the soybean breeding process.

Variability in soybean yield responses to elevated atmospheric CO2: Insights from non-structural carbohydrate remobilisation during seed filling

The increasing atmospheric CO2 concentration (e[CO2]) has mixed effects on soybean most varieties’ yield. This study elucidated the effect of e[CO2] on soybean yield and the underlying mechanisms related to photosynthetic capacity, non-structural carbohydrate (NSC) accumulation, and remobilisation. Four soybean cultivars were cultivated in open-top chambers at two CO2 levels. Photosynthesis rates were determined from R2 to R6. Plants were sampled at R5 and R8 to determine carbohydrate concentrations. There were significant variations in yield responses among the soybean cultivars under e[CO2], from no change in DS1 to a 22% increase in SN14. DS1 and SN14 had the smallest and largest increase, respectively, in daily carbon assimilation capacity. Under e[CO2], DS1, MF5, and XHJ had an increase in Ci, at which point the transition from Rubisco-limited to ribulose-1,5-bisphosphate regeneration-limited photosynthesis occurred, in contrast with SN14. Thus, the cultivars might have distinct mechanisms that enhance photosynthesis under e[CO2] conditions. A positive correlation was between daily carbon assimilation response to e[CO2] and soybean yield, emphasising the importance of enhanced photosynthate accumulation before the R5 stage in determining yield response to e[CO2]. E[CO2] significantly influenced NSC accumulation in vegetative organs at R5, with variation among cultivars. There was enhanced NSC remobilisation during seed filling, indicating cultivar-specific responses to the remobilisation of sucrose and soluble sugars, excluding sucrose and starch. A positive correlation was between leaf and stem NSC remobilisation and yield response to e[CO2], emphasising the role of genetic differences in carbohydrate remobilisation mechanisms in determining soybean yield variation under elevated CO2 levels.

Comparative transcriptome analysis of vegetable soybean grain discloses genes essential for grain quality

BackgroundVegetable soybean is an important vegetable crop in world. Seed size and soluble sugar content are considered crucial indicators of quality in vegetable soybean, and there is a lack of clarity on the molecular basis of grain quality in vegetable soybean.ResultsIn this context, we performed a comprehensive comparative transcriptome analysis of seeds between a high-sucrose content and large-grain variety (Zhenong 6, ZN6) and a low-sucrose content and small-grain variety (Williams 82, W82) at three developmental stages, i.e. stage R5 (Beginning Seed), stage R6 (Full Seed), and stage R7 (Beginning Maturity). The transcriptome analysis showed that 17,107 and 13,571 differentially expressed genes (DEGs) were identified in ZN6 at R6 (vs. R5) and R7 (vs. R6), respectively, whereas 16,203 and 16,032 were detected in W82. Gene expression pattern and DEGs functional enrichment proposed genotype-specific biological processes during seed development. The genes participating in soluble sugar biosynthesis such as FKGP were overexpressed in ZN6, whereas those responsible for lipid and protein metabolism such as ALDH3 were more enhanced in W82, exhibiting different dry material accumulation between two genotypes. Furthermore, hormone-associated transcriptional factors involved in seed size regulation such as BEH4 were overrepresented in ZN6, exhibiting different seed size regulation processes between two genotypes.ConclusionsHerein, we not only discovered the differential expression of genes encoding metabolic enzymes involved in seed composition, but also identified a type of hormone-associated transcriptional factors overexpressed in ZN6, which may regulate seed size and soluble content. This study provides new insights into the underlying causes of differences in the soybean metabolites and appearance, and suggests that genetic data can be used to improve its appearance and textural quality.