Affect Soybean Yield Research Articles

Soybean response to high‐speed planting in Mississippi

AbstractMississippi soybean [Glycine max (L.) Mer.] producers are under pressure to plant as much land as possible within narrow planting windows. The 5‐year average planting progress is 45% of the total at the end of the optimal soybean planting window. New metering and seed delivery technology claims faster planting without sacrificing singulation, stand, or yield, but these tools need to be validated before recommendation. This study aimed to quantify soybean response to planting speeds using a precision planter (John Deere MaxEmerge 2 row units retrofitted with Ag Leader SureSpeed and SureForce) and a mechanical planter (John Deere 1700 ground‐driven mechanical planter equipped with eSet meters) for a total of 7 site‐years across Mississippi. In 2022, both planters were evaluated at four actual ground speeds of 7.9, 10.8, 13.5, and 16.4 km h−1 in a 2 × 4 factorial design. The experimental design was modified in 2023, where the mechanical planter served as the current farmer practice check at 9.7 km h−1and the precision planter speeds were 9.7, 14.5, and 17.7 km h−1 at research station sites and 9.7 and 14.5 km h−1 at an on‐farm site. Across sites, increased planting speed generally increased plant spacing, in‐row spacing variability, and decreased plant population. However, increased speed did not affect soybean yield. The precision planter at 17.7 km h−1 was no different from the mechanical planter in terms of soybean plant population, spacing, and yield in 2023. Results suggest soybean producers can plant soybean at 17.7 km h−1 without compromising yield.

Identification of Insect Pests on Soybean Leaves Based on SP-YOLO

Soybean insect pests can seriously affect soybean yield, so efficient and accurate detection of soybean insect pests is crucial for soybean production. However, pest detection in complex environments suffers from the problems of small pest targets, large inter-class feature similarity, and background interference with feature extraction. To address the above problems, this study proposes the detection algorithm SP-YOLO for soybean pests based on YOLOv8n. The model utilizes FasterNet to replace the backbone of YOLOv8n, which reduces redundant features and improves the model’s ability to extract effective features. Second, we propose the PConvGLU architecture, which enhances the capture and representation of image details while reducing computation and memory requirements. In addition, this study proposes a lightweight shared detection header, which enables the model parameter amount computation to be reduced and the model accuracy to be further improved by shared convolution and GroupNorm. The improved model achieves 80.8% precision, 66.4% recall, and 73% average precision, which is 6%, 5.4%, and 5.2%, respectively, compared to YOLOv8n. The FPS reaches 256.4, and the final model size is only 6.2 M, while the number of computational quantities of covariates is basically comparable to that of the original model. The detection capability of SP-YOLO is significantly enhanced compared to that of the existing methods, which provides a good solution for soybean pest detection. SP-YOLO provides an effective technical support for soybean pest detection.

Sowing Date as a Factor Affecting Soybean Yield—A Case Study in Poland

Soybean is the crop of the future, especially for countries with a high demand for food and feed protein. Therefore, soybean cultivation is moving north to countries at higher latitudes, where temperatures, photoperiodism, and rainfall distribution are not always able to meet soybean requirements. The aim of this study was to evaluate three sowing dates as a factor influencing soybean cultivars yield and seed chemical composition in agroclimatic conditions of south-western Poland. In the years 2016–2019, a field experiment was conducted in Lower Silesia region, near Wroclaw, with three sowing dates: early (mid-April), 10-day delayed (at the turn of April and May), and 20-day delayed (first half of May), and two soybean cultivars: Merlin and Lissabon. In this location, soybean sowing is recommended in mid-April, possibly at the turn of April and May. The cultivars tested differed in yield and yield component values in the years of research, but generally, the Lissabon was better suited to local conditions. Results were discussed with findings of other domestic research, to investigate the problem of the soybean sowing date in Poland. The recommended sowing date for soybean was found to vary from region to region. These differences are due to the length of the growing season in each location and the varied adaptation of cultivars to the local climatic conditions.

Quantifying High-Temperature and Drought Stress Effects on Soybean Growth and Yield in the Western Guanzhong Plain

High-temperature and drought events significantly impact crop growth and development. In the soybean-producing region of the Guanzhong Plain in China, understanding the dynamics of these climatic phenomena is vital for soybean yield preservation. Through a fixed-position observation experiment that analyzed four growth stages, nine agronomic traits, and soybean yield per unit area from 1998 to 2023, this research evaluated the characteristics of high-temperature and drought processes in various growth stages. It also examined the influence of high-temperature processes, drought processes, and their combined effects on agronomic traits and yield. The results indicate the following: (1) High temperature was a constant factor during the soybean growth period, with temperature-related indices markedly surpassing those related to drought. Notably, the occurrence of high-temperature and drought events was more prevalent during the flowering–podding stage than at the podding or grain-filling stages. (2) High temperature profoundly affected soybean yield components, primarily through a decrease in the number of grains per plant during the flowering–podding stage, subsequently impacting the grain weight per plant and yield. In years with extremely high temperatures, the soybean plant height was reduced by 6.1 to 15 cm, the main stem node number decreased by 0.1 to 2.9, the branch number decreased by 0.2 to 0.6, the number of pods per plant decreased by 4.8 to 13.7, the number of grains per pod decreased by 0.1 to 0.3, the number of grains per plant decreased by 13.5 to 32.6, the grain weight per plant decreased by 3.8 to 6.9 g, and the 100-grain weight decreased by 0.1 to 4.5 g. The common impact of high temperature combined with drought processes in different growth stages was reflected in the reduction in the number of branches by 0.1 to 1.4 and the reduction in the number of grains per pod by 0.02 to 13.7. This study underscores the importance of addressing the quantitative effects of climate change and extreme weather on soybean yield, which could help to develop effective adaptation and mitigation strategies.

Yield compensation among plant regions improves soybean adaptation to short-term high-temperature stress during the reproductive period

Extreme heat events prolong the reproductive period and threaten soybean yield, whereas the specific stage at which individual fruits growth is delayed, and yield/yield components at the node, region, and plant levels under short-term heat stress in the reproductive stage are elusive. In this study, heat treatments (40/30 °C) were applied at 0–6 days (HTF0-6), 6–12 days (HTF6-12), 12–18 days (HTF12-18), and 0–12 days (HTF0-12) after the plant's first flower opened, and a control treatment (32/22 °C) was performed. The influences of heat stress on fruit development and yield/yield components at the node, region, and plant levels were investigated. As a result, the growth of individual fruits at nodes was delayed by HTF0-6 and HTF0-12, which was primarily caused by the prolongation of flowering to pods with a length of 2 cm. Interestingly, there were no significant differences in yield between the control treatment and the various high-temperature stress treatments at the plant level. Further analysis of the regional yield of soybean showed that the yield in the bottom and top regions of plants played significant roles in compensating for yield loss in the middle region after HTF0-12. Moreover, the delayed growth of individual fruits in the middle region was negatively correlated with yield. Our results indicate that the prolongation of fruit development induced by HTF0-6 and HTF0-12 may adversely affect soybean yield. However, the spatial compensation of plants could help maintain soybean yield under various short-term high temperature stress treatments during the reproductive period, which should be considered when breeding for and selecting heat-tolerant varieties.

The Use of Biodegradable Film in the Cultivation of Soybean with a Short Growing Season as an Example of Agro-Innovation in a Sustainable Agriculture System

The aim of the study was to assess the yield of three soybean varieties of different earliness classes (Merlin, Coraline, and Viola) grown using two sowing dates (early vs. optimal) and different technologies (soil protected with biodegradable film vs. without soil protection–conventional cultivation). A three-year (2019–2021) field experiment was conducted at the Bayer Technical Advisory Center in Chechło, Poland (50°23′ N 18°44′ E). The three-factor experiment was set up in a randomized split-plot design in three replicates. The experimental factors were (i) sowing date, (ii) cultivar, and (ii) cultivation technology. The effect of agrotechnical factors and their interaction with the weather on selected biometric traits and seed yield was assessed. The results indicated that the weather conditions and its interaction with agrotechnical factors significantly influenced the biometric traits and seed yield of soybean. Optimal hydrothermal conditions significantly increased analyzed parameters and seed yield. However, too much rainfall in August had negative effects on biometric traits. It was proved that, early sowing adversely affected soybean yield. Sowing at the optimal date, i.e., the end of April, resulted in a yield of 3.8 t ha−1. The use of biodegradable film in the year with more rainfall increased soybean yield by 1 t ha−1 compared to the year with less rainfall. The early ‘Merlin’ cultivar grown in the system with biodegradable film produced significantly more pods and seeds per plant and a higher pod weight per plant. The cultivars with a longer growing season (‘Viola’ and ‘Coraline’) responded negatively to cultivation in the modern technology. The use of biodegradable film is recommended for cultivars with a short growing season, sown at the optimal time and in regions with moderate to high rainfall totals during the growing season.

Leaf shape, planting density, and nitrogen application affect soybean yield by changing direct and diffuse light distribution in the canopy

When attempting to maximize the crop yield from field-grown soybean (Glycine max (L.) Merr.) by means of improving the light conditions for photosynthesis in the canopy, it is crucial to find the optimal planting density and nitrogen application rate. The soybean plants that were the subject of our experiment were cultivated in N-dense mutual pairs, and included two cultivars with different leaf shapes; one cultivar sported ovate leaves (O-type) and the other lanceolate leaves (L-type). We analyzed the results quantitatively to determine the amount of spatial variation in light distribution and photosynthetic efficiency across the canopy, and to gauge the effect of the experimental parameters on the yield as well as the photosynthetic light and nitrogen use efficiency of the crop. Results indicate that the different leaf shapes were responsible for significant disparities between the photosynthetic utilization of direct and diffuse light. As the nitrogen fertilizer rate and the planting density increased, the soybean plants responded by adjusting leaf morphology in order to maximize the canopy apparent photosynthetic light use efficiency, which in turn affected the leaf nitrogen distribution in the canopy. Despite the fact that the light interception rate of the canopy of the L-type cultivar was lower than that of the canopy of the O-type cultivar, we found its canopy apparent photosynthetic nitrogen and light use efficiency were higher. It was interesting to note, however, that the nitrogen and light use efficiency contributions associated with exposure to diffuse light were greater for the latter than for the former.

Use of interpretive machine learning and a crop model to investigate the impact of environment and management on soybean yield gap

Context Management and environmental conditions are the main factors influencing yield of soybean (Glycine max (L.) Merr.). Despite an increase in average soybean yield in recent years in Iran, a considerable gap remains between actual yield and potential yield. Aims The objective of this study was to identify critical climate and management factors affecting soybean yield in Iran’s major soybean production area. Methods A combination of machine learning approaches (using gradient boosted decision trees, XGBoost) and the SSM-iCrop2 simulation model was used. Critical management factors affecting soybean yield were determined through interpretive machine learning using information collected from 268 soybean fields over a 5-year period. Potential yield and water-limited potential yield at six weather stations were estimated for 30 years via the SSM-iCrop2 simulation model. Water limitation was determined by considering the ratio of water-limited yield potential to potential yield, and heat stress status was quantified as the number of days with maximum temperature >36°C during the soybean growing season. Key results The XGBoost models adequately described the observed changes in soybean yield. Root-mean-square error and Lin’s concordance correlation coefficient values of the calibrated model were 262 kg ha−1 and 0.96, respectively, which indicated that the predictor variables could describe most of the variation in soybean yield for the studied dataset. Conclusions We identified 15 climatic and management variables that affect soybean yield. A large part of the studied area is under high water stress and low heat stress. Implications Optimal planting date and improved irrigation management are the main options for reducing the yield gap in the study area.

Evaluating the Effects of Flooding Stress during Multiple Growth Stages in Soybean

Flooding is becoming an increasing concern for soybean (Glycine max [L.] Merr.) production worldwide due to the sensitivity of most cultivars grown today to flood stress. Flooding can stunt plant growth and limit yield, causing significant economic loss. One sustainable approach to improve performance under flood stress is to develop flood-tolerant soybean cultivars. This study was conducted to evaluate soybean genotypes for the response to flood stress at three critical growth stages of production—germination, early vegetative growth (V1 and V4), and early reproductive growth (R1). The results demonstrated that stress imposed by flooding significantly affected soybean yield for each growth stage studied. The average germination rate over the various treatments ranged from 95% to 46%. Despite the poor germination rates after the extended flood treatments, the flood-tolerant genotypes maintained a germination rate of >80% after 8 h of flooding. The germination rate of the susceptible genotypes was significantly lower, ranging 58–63%. Imposing flood stress at the V1 and V4 growth stage also resulted in significant differences between the tolerant and susceptible genotypes. Genotypes with the highest level of flood tolerance continually outperformed the susceptible genotypes with an average 30% decrease in foliar damage based on visual scoring and a 10% increase in biomass. The yield of the tolerant genotypes was also on average 25% higher compared to the susceptible genotypes. These results suggest that breeding for flood tolerance in soybean can increase resiliency during crucial growth stages and increase yield under flood conditions. In addition, the genotypes developed from this research can be used as breeding stock to further make improvements to flood tolerance in soybean.

Identification of QTL, QTL-by-environment interactions, and their candidate genes for resistance HG Type 0 and HG Type 1.2.3.5.7 in soybean using 3VmrMLM.

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is an important disease affecting soybean yield in the world. Potential SCN-related QTLs and QTL-by-environment interactions (QEIs) have been used in SCN-resistant breeding. In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTLs and QEIs for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7 in 156 different soybean cultivars materials. The results showed that 53 QTLs were detected in single environment analysis; 36 QTLs and 9 QEIs were detected in multi-environment analysis. Based on the statistical screening of the obtained QTLs, we obtained 10 novel QTLs and one QEI which were different from the previous studies. Based on previous studies, we identified 101 known genes around the significant/suggested QTLs and QEIs. Furthermore, used the transcriptome data of SCN-resistant (Dongnong L-10) and SCN-susceptible (Suinong 14) cultivars, 10 candidate genes related to SCN resistance were identified and verified by Quantitative real time polymerase chain reaction (qRT-PCR) analysis. Haplotype difference analysis showed that Glyma.03G005600 was associated with SCN HG Type 0 and HG Type 1.2.3.5.7 resistance and had a haplotype beneficial to multi-SCN-race resistance. These results provide a new idea for accelerating SCN disease resistance breeding.

Sustainability of cover cropping practice with changing climate in Illinois

Climate change could adversely impact the best management practices (BMPs) designed to build a sustainable agro-ecological environment. Cover cropping is a conservation practice capable of reducing nitrate-nitrogen (NO3–N) loadings by consuming water and nitrate from the soil. The objective of this study was to investigate how climate change would impact the proven water quality benefits of cereal rye as a winter cover crop (CC) over the climate divisions of Illinois using the DSSAT model. Moreover, this study explores the sustainability of the CC with the changing climate conditions by using five regional climate models (RCMs) projections of two warming scenarios-rcp45 (a medium emission scenario - radiative forcing of 4.5 W/m2) and rcp85 (a high emission scenario - radiative forcing of 8.5 W/m2)). The CC impact simulated in the warming scenarios for the near-term (2021–2040) and the far-term future (2041–2060) were compared with the baseline scenario (2001–2020). Our results conclude that the climate change may negatively impact [average of CC and no CC (NCC)] maize yield (−6.6%) while positively affecting soybean yield (17.6%) and CC biomass (73.0%) by the mid-century. Increased mineralization caused by rising temperature could increase the nitrate loss via tile flow (NLoss) and nitrate leached (NLeached) up to 26.3% and 7.6% on average by the mid-century in Illinois. Increasing CC biomass could reduce the NLoss more considerably in all the scenarios compared to the baselines. Nevertheless, the NLoss level in the CC treatment can increase from the near-term to far-term future and could get closer to the baseline levels in the NCC treatment. These results suggest that CC alone may not address nitrate loss goals via subsurface drainage (caused by increasing N mineralization) in future. Therefore, more robust and cost-effective BMPs are needed to aid the CC benefits in preventing nutrient loss from the agricultural fields.

Soybean Disease Detection and Segmentation Based on Mask-RCNN Algorithm

Anthracnose, frogeye leaf spot (FLS), rhizoctonia aerial blight (RAB), soybean mosaic virus (SMV), and yellow mosaic virus (YMV) of soybean are major common soybean leaf diseases that seriously affect soybean yield in India. However, the existing system needs a real-time detection method for soybean leaf diseases, which will help to take appropriate action for disease cure with minimum losses. This study studied a real-time detector for soybean leaf diseases based on deep convolutional neural networks. The 3,127 RGB images of disease-free leaves, anthracnose, FLS, RAB, SMV, and YMV-affected leaves of soybean were collected from the agriculture fields. The Mask R-CNN detection algorithm was used for the detection of soybean leaf diseases by introducing the Res Net 50 module. The pre-processed images (512×512 pixels) were used as input in Mask R-CNN. The model was trained at a number of epochs, training step per epoch training & validation, and learning rate were 80, 500, 50, 8, and 0.001, respectively. The detection accuracy was calculated at three levels of minimum detection confidence i.e. 0.80, 0.85, and 0.90. The results indicate that the maximum detection accuracy i.e. greater than 85% at 0.90 level of minimum detection confidence. This research indicates that the real-time detector based on deep learning provides a feasible solution for diagnosing soybean leaf diseases and provides guidance for the detection of other plant diseases. In addition to that the application of pesticide in the early stage reduce the use of pesticide resulting in less environmental pollution.

Interaksi Jarak Tanam dan Mulsa Jerami terhadap Produksi Tanaman Kedelai (Glycine max (L.) Merill)

The low yield of soybeans is caused by drought, soft organic matter, and space for irregular plant growth. This study examines how plant spacing, and straw mulch dosage affect soybean yield. The research was conducted from August to December 2021 at the Field Laboratory of the Faculty of Agriculture, UHO. The research tools used in this study were tractors, hoes, ruler meters, ovens, analytical scales, and other supporting equipment. The materials consisted of soybean of the Anjasmoro variety, straw mulch, and other supporting materials. The study used a split-plot design arranged in a randomized block design. The main plot is the spacing (J) which consists of 2 levels, namely 20 cm x 20 cm (J1) and 20 cm x 30 cm (J2). Subplots were the dose of straw mulch (M) which consisted of three levels, namely without straw mulch (M0), 4 tons ha-1(M1), and 8 tons ha-1 (M2). The observed variables included the total number of pods per plant, weight of 100 seeds (g), seed weight per plant (g), harvest index (IP), and yield (tons). ANOVA analyzed data, and statistically different were tested by DMRT (α=0.05) using SAS 9.2. The interaction of plant spacing and straw mulch an increasing: the total number of pods, the weight of 100 grains, grain weight, harvest index, and yield. Plant spacing of 20 cm x 30 cm and the straw mulch of 8 t ha-1 (J2M2) obtained a product of 2.18 t ha-1 (an increase of 44.4%) compared to a spacing of 20 cm x 20 cm and without straw mulch (J1M0).

Morpho-physiological and Biochemical Fingerprints of the Soybean Agroforestry System in Different Crop Rotation Models

Background: Soybean cultivars grown on various crop rotation models in the agroforestry system with kayu putih (Melaleuca cajuputi) have shown different yields per hectare. However, no information related to morpho-physiological and biochemical fingerprints affecting soybean yield has been found. Thus, this study aimed to determine the morpho-physiological and biochemical fingerprints and their effect on the soybean agroforestry system in different crop rotation models through multivariate analysis. Methods: The study was conducted during the dry season (March-June 2021) and the wet season (November 2021-February 2022) in Menggoran Forest Resort, Playen Forest Section, Yogyakarta Forest Management District, Indonesia. The morpho-physiological and biochemical variables of 15 soybean cultivars were evaluated using four crop rotation models. Observations included 22 morphological, physiological and biochemical variables of soybean. Data were analyzed using ANOVA, PCA-biplot, heatmap cluster, factor analysis, SEM-PLS and standardized stepwise regression. Result: Results showed four groups of soybean cultivars and three groups of crop rotation models based on morpho-physiological and biochemical fingerprints. Morpho-physiological and biochemical fingerprints of soybean can be differentiated based on root surface area, nitrogen content and superoxide dismutase.

Transcriptome analysis showed the metabolic pathway of differentially expressed genes (DEGs) in resistant and susceptible soybean (Glycine max) to sclerotinia stem rot (SSR) and candidate gene mining

Context Sclerotinia stem rot (SSR) is one of the diseases that seriously affect soybean yield, leading to heavy losses all over the world. A well-known SSR resistant variety is ‘Maple Arrow’. Aims In this study, transcriptome sequencing analysis of resistant variety ‘Maple Arrow’ and susceptible variety ‘Hefeng25’ was conducted to understand the resistance mechanism of resistant and susceptible soybean varieties to SSR and to look for candidate genes. Methods RNA sequencing of Maple Arrow and Hefeng25 generated 75.09 GB and 64.97 GB clean readings, respectively. In total, 417 differentially expressed genes (DEGs) were found among the different comparable groups. Gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis and haplotype analysis were performed for genes with different expression levels in Maple Arrow and Hefeng25. Key results It was found that DEGs from Maple Arrow and Hefeng25 were involved in the regulation of ‘oxidation–reduction process’, ‘regulation of transcription’, ‘amino acid metabolism’, ‘methylation’ and ‘membrane’, ‘integral component of membrane’ and ‘epidermal growth-factor receptor substrate 15’. In total, 31 haplotypes of 12 genes were screened out with significant or extremely significant differences among soybeans with different levels of SSR resistance. Conclusions These genes may be involved in the relevant pathways of soybean sclerotiniose. Implications To provide excellent gene resources for further disease-resistance breeding.

Row spacing and irrigation management affect soybean yield, water use efficiency and economics

Declining water levels in the Mississippi River Valley Alluvial Aquifer necessitates research on the management practices that can reduce irrigation water use and improve irrigation water use efficiency. A three-year field study from 2019 to 2021 evaluated the effects of row spacing and irrigation management on soybean seed yield and quality, irrigation water use efficiency (IWUE), water productivity (WP), and profitability. Soybean was planted in two row spacings [single- (SR) and twin-row (TR)] and irrigated with three different methods [every row irrigated (ERI), alternate row irrigated (ARI), non-irrigated (NI)]. Soybean yields were increased with application of irrigation either as ERI or ARI as compared to NI in all three years ranging from 20% to 79%. The ERI had 20% increase in yield over ARI in 2021, whereas no significant differences were found between ERI and ARI in 2019 and 2020. Averaged over row spacing, IWUE was 66–91% greater with ARI compared to ERI. Only 2021 showed an increase (34%) in WP with irrigation applied as ARI than NI. Differences in rainfall amount among years and amount of irrigation applied under different treatments affected the soybean yields. Soybean yield showed reduction by 0.37–1.2 kg ha−1 with each unit increase in cumulative stress (kPa-days) for soil moisture potential drier than the −40 kPa on very fine sandy loam soils. Twin-row soybean had higher expected returns in all cases except for the NI and ERI in 2021. The TR soybean produced the greatest average risk-return than other treatments. The ARI with TR had the second highest overall risk-return trade-off. The ARI resulted in an equivalent risk-return production system as ERI for SR soybeans. Alterations in furrow irrigation methods could be more beneficial than row spacing to improve soybean productivity and IWUE.

Soil quality measurement for sustainable soybean yield agroforestry system under different crop rotation models

Abstract. Alam T, Suryanto P, Handayani S, Supriyanta, Wulandari RA, Anshari A, Aisya AW, Purba AE, Widowati R, Taryono. 2022. Soil quality measurement for sustainable soybean yield agroforestry system under different crop rotation models. Biodiversitas 23: 6155-6163. Soil quality is essential for sustaining the soybean yield in an agroforestry system. This study determined the soil quality variables that affect soybean yield under different crop rotation models in the agroforestry system with kayu putih (Melaleuca cajuputi). A 2-year experiment was conducted during the dry season (March-June 2021) and the wet season (November 2021-February 2022) at Menggoran Forest Resort, Playen District, Gunungkidul Regency, Special Province of Yogyakarta, Indonesia. All of the trials during each season were laid out in a randomized complete block design with three blocks as replication. The treatment crop rotation model consisted of soybean planting after fallow (FS), soybean planting after maize (MS), soybean planting after rice (RS), and continuous soybean planting (SS). The observations were soybean yield and 21 soil quality variables. The data were analyzed using analysis of variance, multiple factor analysis, heatmap cluster, generalized pair plot, and standardized stepwise regression. The increase in soybean yield during the dry season was affected by TB, Fe, and N in the soil, while it was affected by TF, Fe, and silt during the wet season. Soil quality and soybean yield can be improved by applying organic matter and soil amendment.

Cover crops affect soybean yield components, but not grain quality

AbstractCover cropping is mainly used to prevent soil erosion, but the effects of individual cover crops on the subsequent soybean [Glycine max (L.) Merr.] are hardly known. Therefore, two annual field trials with 15 cover crop species and bare fallow as control were conducted in semi‐arid East Austria in 2016 and 2017. Cover crops were sown in July in 3‐ × 10‐m plots in a randomized block design with three replicates and were incorporated in mid‐April before soybean were seeded. Cover crop biomass differed between plant species and year (2016, 626 ± 390; 2017, 84 ± 86.1 g m−2). Aboveground cover crop biomass did not affect soybean establishment, but single cover crop species such as ryegrass (Lolium multiflorum Lam. var. westerwoldicum Wittm.), kidney vetch (Anthyllis vulneraria L.), and corn cockle (Agrostemma githago L.) impeded soybean seeding due to high root densities and prevented germination or fostered outbreak of two‐spotted spider mites (Tetranychus urticae Koch). These three cover crops reduced soybean grain yield by 12–84%. Grain yield was a function of grain density m−2 in a three‐way interaction with year and cover crop treatment, but not by plant or pod density. Protein, oil, and sucrose content of soybean were unaffected by cover crop treatments. Overall, with the exception of ryegrass, kidney vetch, and corn cockle, cover crops can be grown ahead of soybean without any yield loss and can be used for their versatile ecosystem services.

Narrow-row soybean and a cereal rye cover crop suppress glyphosate-resistant horseweed (Conyza canadensis)

AbstractAlternative strategies are needed for management of glyphosate-resistant (GR) horseweed in soybean. Integrating a cereal rye cover crop with soybean planted in narrow rows may improve control and reduce herbicide selection pressure for herbicide-resistant horseweed biotypes. Four site-years of experiments were conducted in Michigan to determine whether fall-planted cereal rye terminated with glyphosate 1 wk prior to (early termination) or 1 wk after (planting green) planting in combination with narrow-row soybean improved GR horseweed management. At postemergence (POST) herbicide application, horseweed biomass was reduced by 71% to 90% when soybean was planted into cereal rye, regardless of termination time, compared with no cover across all row widths. Planting green or narrow-row soybean suppressed horseweed through soybean harvest. When glyphosate was applied POST (noneffective), horseweed biomass was 36% to 46% lower when planting green compared with early terminated cereal rye and no cover. Similarly, planting soybean in 19- and 38-cm rows reduced horseweed biomass by 48% and 28%, respectively, compared with 76-cm rows. Cereal rye did not affect soybean yield pooled over 3 of 4 site-years; however, narrow row soybean yielded 11% to 18% higher than 76-cm rows. Soybean yield was 11% higher when an effective POST herbicide was applied. In conclusion, fall-seeded cereal rye or narrow-row soybean suppressed horseweed compared with no cover and 76-cm rows; however, the effects of early termination did not last throughout the growing season in most cases. Delaying cover crop termination by planting green reduced horseweed biomass and density through soybean harvest, but reduced yield in 1 site-year due to an increased incidence of white mold. These cultural practices have a positive influence on suppressing horseweed that should be part of an overall horseweed management strategy; however, the use of an effective POST herbicide is still needed for complete season-long horseweed management.

Early-season plant-to-plant spatial uniformity can affect soybean yields

Increased soybean (Glycine max L. Merril) seed costs have motivated interest in reduced seeding rates to improve profitability while maintaining or increasing yield. However, little is known about the effect of early-season plant-to-plant spatial uniformity on the yield of modern soybean varieties planted at reduced seeding rates. The objectives of this study were to (i) investigate traditional and devise new metrics for characterizing early-season plant-to-plant spatial uniformity, (ii) identify the best metrics correlating plant-to-plant spatial uniformity and soybean yield, and (iii) evaluate those metrics at different seeding rate (and achieved plant density) levels and yield environments. Soybean trials planted in 2019 and 2020 compared seeding rates of 160, 215, 270, and 321 thousand seeds ha−1 planted with two different planters, Max Emerge and Exact Emerge, in rainfed and irrigated conditions in the United States (US). In addition, trials comparing seeding rates of 100, 230, 360, and 550 thousand seeds ha−1 were conducted in Argentina (Arg) in 2019 and 2020. Achieved plant density, grain yield, and early-season plant-to-plant spacing (and calculated metrics) were measured in all trials. All site-years were separated into low- (2.7 Mg ha−1), medium- (3 Mg ha−1), and high- (4.3 Mg ha−1) yielding environments, and the tested seeding rates were separated into low (< 200 seeds m−2), medium (200–300 seeds m−2), and high (> 300 seeds m−2) levels. Out of the 13 metrics of spatial uniformity, standard deviation (sd) of spacing and of achieved versus targeted evenness index (herein termed as ATEI, observed to theoretical ratio of plant spacing) showed the greatest correlation with soybean yield in US trials (R2 = 0.26 and 0.32, respectively). However, only the ATEI sd, with increases denoting less uniform spacing, exhibited a consistent relationship with yield in both US and Arg trials. The effect of spatial uniformity (ATEI sd) on soybean yield differed by yield environment. Increases in ATEI sd (values > 1) negatively impacted soybean yields in both low- and medium-yield environments, and in achieved plant densities below 200 thousand plants ha−1. High-yielding environments were unaffected by variations in spatial uniformity and plant density levels. Our study provides new insights into the effect of early-season plant-to-plant spatial uniformity on soybean yields, as influenced by yield environments and reduced plant densities.