Soybean Growth Stages Research Articles

UAV-Based Remote Sensing for Soybean FVC, LCC, and Maturity Monitoring

Timely and accurate monitoring of fractional vegetation cover (FVC), leaf chlorophyll content (LCC), and maturity of breeding material are essential for breeding companies. This study aimed to estimate LCC and FVC on the basis of remote sensing and to monitor maturity on the basis of LCC and FVC distribution. We collected UAV-RGB images at key growth stages of soybean, namely, the podding (P1), early bulge (P2), peak bulge (P3), and maturity (P4) stages. Firstly, based on the above multi-period data, four regression techniques, namely, partial least squares regression (PLSR), multiple stepwise regression (MSR), random forest regression (RF), and Gaussian process regression (GPR), were used to estimate the LCC and FVC, respectively, and plot the images in combination with vegetation index (VI). Secondly, the LCC images of P3 (non-maturity) were used to detect LCC and FVC anomalies in soybean materials. The method was used to obtain the threshold values for soybean maturity monitoring. Additionally, the mature and immature regions of soybean were monitored at P4 (mature stage) by using the thresholds of P3-LCC. The LCC and FVC anomaly detection method for soybean material presents the image pixels as a histogram and gradually removes the anomalous values from the tails until the distribution approaches a normal distribution. Finally, the P4 mature region (obtained from the previous step) is extracted, and soybean harvest monitoring is carried out in this region using the LCC and FVC anomaly detection method for soybean material based on the P4-FVC image. Among the four regression models, GPR performed best at estimating LCC (R2: 0.84, RMSE: 3.99) and FVC (R2: 0.96, RMSE: 0.08). This process provides a reference for the FVC and LCC estimation of soybean at multiple growth stages; the P3-LCC images in combination with the LCC and FVC anomaly detection methods for soybean material were able to effectively monitor soybean maturation regions (overall accuracy of 0.988, mature accuracy of 0.951, immature accuracy of 0.987). In addition, the LCC thresholds obtained by P3 were also applied to P4 for soybean maturity monitoring (overall accuracy of 0.984, mature accuracy of 0.995, immature accuracy of 0.955); the LCC and FVC anomaly detection method for soybean material enabled accurate monitoring of soybean harvesting areas (overall accuracy of 0.981, mature accuracy of 0.987, harvested accuracy of 0.972). This study provides a new approach and technique for monitoring soybean maturity in breeding fields.

Diamide seed treatment may protect early soybean growth stages against Helicoverpa armigera

In intensive soybean cropping systems, early-season pest insects such as lepidopteran larvae can seriously compromise crop establishment. Using a polyphagous global pest ̶̶ the Old World bollworm Helicoverpa armigera (Lepidoptera: Noctuidae), we determined the potential of seed treatments with diamide or neonicotinoid-carbamate mixture for systemic plant protection and residual efficacy against noctuid larvae. In controlled edaphoclimatic conditions, we infested differently aged soybean plants and recorded plant damage and insect mortality, growth, development, and fecundity to assess lethal and sublethal post-exposure effects. The seed treatments reduced plant damage by 50% during 1–7 days after plant emergence. The lowest damage (<25% defoliation) occurred on plants treated with chlorantraniliprole or cyantraniliprole. These diamides caused larval mortality >80% until 14 days after soybean emergence, which was the window of residual control efficacy. Carbofuran or thiodicarb + imidacloprid caused less than 50% larval mortality and minor plant protection against the larvae. There were sublethal effects on the growth, development, and fecundity of the insects surviving on plants of the insecticide seed treatments. The diamide seed treatments reduced the overall insect fitness for a longer time after plant emergence than did the other treatments. Therefore, soybean seed treatment with chlorantraniliprole or cyantraniliprole is potentially efficacious against noctuid larvae in the VE–V1 soybean growth stages (emergence until the first trifoliate), causing lethal and sublethal effects on the insects. Judicious use and effective resistance management for these systemic compounds are relevant as they are helpful for seed treatment and foliar applications against diverse targets.

Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index

Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R2) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages.

Temporal loss of fungal taxa driven by drift contributes to community stability during soybean development

Understanding the assembly mechanism of fungal communities is of great importance for conserving and managing mycobiomes. However, the ecological processes underlying the decline in fungal community size remain poorly understood. Herein, we characterized soil fungal assembly processes at three community levels (stage-specific absent taxa, remaining taxa, and whole taxa) using ITS sequencing data. The bulk and rhizospheric soil samples were collected at five growth stages of soybeans, which grew in soil with 16-year fertilization. An alpha diversity analysis revealed that rhizospheric communities decreased fungal richness during the soybeans' developmental transitions. Some plant pathogens in the phylum Ascomycota showed stage-specific loss in the soybeans' rhizosphere. The neutral model explained 34–40 % of the demographic variations of the remaining subcommunities and the whole communities. Their assembly processes were primarily governed by dispersal limitation in both sampling compartments and secondarily governed by variable selection and drift in the bulk soil and rhizosphere, respectively. The temporally missing subcommunities were nonneutral and primarily dominated by drift. Moreover, both the highly connected network nodes and the temporally missing taxa promoted the attack robustness of fungal communities. Overall, these results advance our understanding of the assembly of different subsets of fungal communities and provide new insight into the maintenance mechanisms of metacommunity stability.

Microplastics affect soybean rhizosphere microbial composition and function during vegetative and reproductive stages

Microplastics (MPs) are emerging contaminants in agricultural soil, whereas their effects on the rhizosphere microbial ecosystems and biogeochemical nitrogen cycles during plant growth remain unknown. Here, a 70-day greenhouse experiment was carried out with black and fluvo-aquic soil to evaluate the influence of polyamide (PA), polyethylene (PE), polyester (PES), and polyvinyl chloride (PVC) MPs on the bacterial communities and functions in the soybean rhizosphere. The PA treatment consistently affected the rhizobacterial alpha diversity in the fluvo-aquic soil at soybean vegetative and reproductive growth stages, whereas the PE, PES, and PVC treatments had a short-term effect on the bacterial alpha diversity. At two growth stages, 6 and 23 biomarkers were consistently abundant in the PA treatment in the black soil and fluvo-aquic soil, respectively, and order Rhizobiales was found to be a biomarker for PA MPs contamination in both soils. Additionally, PA treatment decreased bacterial network complexity and tightness, whereas the effects of the PE, PES, and PVC on bacterial co-occurrence patterns varied depending on the soil types. Furthermore, PES and PVC treatments inhibited ammonification processes in the soybean rhizosphere, and PE could temporarily inhibit ammonia oxidation and denitrification processes according to the variations of N-cycling gene abundances. These effects on soil N-cycling also varied with soil types and soybean growth stages. This study provides profound information for understanding of MPs residues on the assembly of the soybean rhizosphere communities and function during plant development.

Effects of Foliar Fertilizer Applications on Leaf Area, Chlorophyll and Nutritional Content at Different Growth Stages of Soybean

Optimum büyümenin sağlanması ve yüksek verim için bitkiler, bitki besin elementlerinin toprakta alınabilir formda ve yeterince var olmasına gereksinim duymaktadır. Ancak, Akdeniz Bölgesi gibi kireç içeriği ve pH’sı yüksek topraklarda bulunan makro ve mikro besin elementlerinin bitkiler tarafından alımı kısıtlı olmakta, bazı durumlarda ise bitki tarafından kullanılamamaktadır. Bu gibi topraklarda gerekli besin elementinin, özellikle mikro bitki besin elementlerinin yaprak gübresi şeklinde verilmesi bitkinin daha hızlı ve etkili şekilde faydalanmasını sağlamaktadır. Bu çalışmada, soya bitkisinin (Glycine max L.) farklı gelişim dönemlerinde (V3, R1, R3) yaprak gübresi şeklinde verilen çinko ve demir besin elementlerinin klorofil içeriği (SPAD), yaprak alan indeksi (LAI) ve soya bitkisinin kimyasal kompozisyonuna etkisi incelenmiştir. Deneme, 2018 ve 2019 yıllarında ikinci ürün olarak Amik Ovası (Hatay) koşullarında bölünen bölünmüş parseller deneme desenine göre üç yinelemeli olarak yürütülmüştür. Ana parsellere çeşitler (Arısoy, Bravo, Nazlıcan), alt parsellere gelişim dönemleri (V3, R1, R3) ve alt alt parsellere gübre uygulamaları (Kontrol, Çinko, Demir, Çinko + Demir) yerleştirilmiştir. Soya bitkisine farklı gelişim dönemlerinde uygulanan demir ve çinko yaprak gübreleri, kontrol grubuna göre SPAD ve LAI değerlerini artırmıştır. En yüksek klorofil içeriği (SPAD değeri) Nazlıcan x R3 x Zn + Fe (36.03) uygulamasından alınırken, en yüksek yaprak alan indeksi değeri ise Bravo x V3 x Fe (7.57) uygulamasından alınmıştır. Klorofil içeriği tespitinde (SPAD değerleri) generatif dönem ölçümlerinde yüksek sonuç alınırken, yaprak alan indeksi ölçümlerinde ise vejetatif dönem daha iyi sonuç vermiştir. Yaprak besin elementi içerikleri yönünden incelendiğinde ise generatif dönemde yapılan uygulamalar neticesinde çinko ve demir birikimleri daha fazla olmuştur. Sonuç olarak, kireçli topraklarda soya bitkisinin ihtiyacı olan mikro besin elementlerinin yaprak gübrelemesi ile karşılanabileceği görülmüştür.

The effect of drought stress on nodulation, plant growth, and nitrogen fixation in soybean during early plant growth

AbstractSoybean [Glycine max (L.) Merr.] is one of the most important grain legume crops in the world. Soybean forms a symbiotic relationship with the nitrogen‐fixing rhizobia bacteria that reside in root nodules and fix atmospheric nitrogen. Most of the soybean's nitrogen demand is derived from symbiotic nitrogen fixation (SNF). Drought is becoming a major threat to crop production in the world. Legumes are sensitive to drought stress, and drought can negatively affect SNF, grain yield, and seed protein production. The effect of drought stress on soybean growth, morphological traits, nodulation, water use efficiency, and SNF, particularly during early plant growth, remains poorly characterized. We hypothesized that early‐onset drought stress can negatively affect nodulation, plant growth, and SNF in soybean during the vegetative growth stages. To test this hypothesis, a controlled‐environment study was conducted using soybean plants grown in pots, where plants were subjected to moderate drought stress (40% field capacity) and well‐watered (80% field capacity) from seeding until the flowering stage. Drought stress significantly reduced the nodule number and nodule dry weight, root and shoot biomass, shoot total nitrogen content, shoot carbon to nitrogen ratio, and shoot total fixed nitrogen compared to the well‐watered control. Conversely, root‐to‐shoot ratio and shoot nitrogen concentration were higher under drought stress compared to well‐watered conditions. These findings enhance our understanding of adverse impacts of drought in the early growth stage of soybean while suggesting that reduced total nitrogen assimilation during the vegetative phase can lead to less nitrogen reserves available for translocating at the seed filling stages for seed protein production. This information should aid in generating drought‐tolerant soybean cultivars.

Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation

Leaf Area Index (LAI) is an important parameter which can be used for crop growth monitoring and yield estimation. Many studies have been carried out to estimate LAI with remote sensing data obtained by sensors mounted on Unmanned Aerial Vehicles (UAVs) in major crops; however, most of the studies used only a single type of sensor, and the comparative study of different sensors and sensor combinations in the model construction of LAI was rarely reported, especially in soybean. In this study, three types of sensors, i.e., hyperspectral, multispectral, and LiDAR, were used to collect remote sensing data at three growth stages in soybean. Six typical machine learning algorithms, including Unary Linear Regression (ULR), Multiple Linear Regression (MLR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Back Propagation (BP), were used to construct prediction models of LAI. The results indicated that the hyperspectral and LiDAR data did not significantly improve the prediction accuracy of LAI. Comparison of different sensors and sensor combinations showed that the fusion of the hyperspectral and multispectral data could significantly improve the predictive ability of the models, and among all the prediction models constructed by different algorithms, the prediction model built by XGBoost based on multimodal data showed the best performance. Comparison of the models for different growth stages showed that the XGBoost-LAI model for the flowering stage and the universal models of the XGBoost-LAI and RF-LAI for three growth stages showed the best performances. The results of this study might provide some ideas for the accurate estimation of LAI, and also provide novel insights toward high-throughput phenotyping of soybean with multi-modal remote sensing data.

Effect of Phosphorus Supply Levels on Nodule Nitrogen Fixation and Nitrogen Accumulation in Soybean (Glycine max L.)

The specific mechanism by which phosphorus affects nodule nitrogen fixation and nitrogen absorption in soybeans remains inconclusive. To further quantitatively analyze the effect of phosphorus on nodule nitrogen fixation and nitrogen accumulation in soybeans, this experiment was carried out under sand culture conditions. The experiment consisted of six phosphorus supply levels (1 mg/L, 11 mg/L, 21 mg/L, 31 mg/L, 41 mg/L, 51 mg/L). The acetylene reduction method and 15N tracer method (50 mg/L (NH4)2SO4) were used to determine and analyze the nodule growth status, nodule nitrogenase activity, nitrogen content, and nodule nitrogen fixation rate at initial flowering (R1 stage), initial pod (R3 stage), seed filling (R5 stage) and maturity stages (R8 stage). The results are described as follows: 1. The nitrogen fixation of soybean nodules at different growth stages has different requirements for phosphorus supply levels. The initial flowering stage and seed-filling stage were 31 mg/L–41 mg/L, and the initial pod stage was 51 mg/L. 2. The nitrogen source in different parts of soybean showed different trends with different growth periods and phosphorus supply concentrations. Among them, from the initial flowering stage to the seed filling stage, the main body of the nitrogen supply of soybean shoots in the low phosphorus treatment (1 mg/L–31 mg/L) gradually changed from fertilizer nitrogen to nodule nitrogen fixation, while the main body of the nitrogen supply of soybean shoots in the high phosphorus treatment (41 mg/L–51 mg/L) always showed nodule nitrogen fixation and was transformed into fertilizer nitrogen at the mature stage. The main nitrogen supply to the roots of soybean at different levels of phosphorus supply from the initial flowering to the initial pods and maturity stage was fertilizer nitrogen, and the main nitrogen supply at the seed filling stage was nodule nitrogen fixation. The nitrogen supply to the main body of soybean nodules was constantly nodule nitrogen fixation. 3. Different phosphorus supply levels significantly affected the nitrogen fixation of soybean nodules (R2 ≥ 0.803), and both the acetylene reduction method and the 15N tracer method could be used to determine the nitrogen fixation capacity of soybean nodules. This study indicated the optimal phosphorus supply level of nodules in different growth stages of soybean and clarified the main body of phosphorus supply in different parts of soybean at different growth stages, which pointed out the direction for further improving the utilization efficiency of soybean nitrogen and phosphorus fertilizer.

Physiological and metabolic analyses provide insight into soybean seed resistance to fusarium fujikuroi causing seed decay.

Seed-borne pathogens cause diverse diseases at the growth, pre- and post-harvest stage of soybean resulting in a large reduction in yield and quality. The physiological and metabolic aspects of seeds are closely related to their defense against pathogens. Recently, Fusarium fujikuroi has been identified as the dominant seed-borne fungi of soybean seed decay, but little information on the responses of soybean seeds induced by F. fujikuroi is available. In this study, a time-course symptom development of seed decay was observed after F. fujikuroi inoculation through spore suspension soaking. The germination rate and the contents of soluble sugar and soluble protein were significantly altered over time. Both chitinase and β-1,3-glucanase as important fungal cell wall-degrading enzymes of soybean seeds were also rapidly and transiently activated upon the early infection of F. fujikuroi. Metabolic profile analysis showed that the metabolites in glycine, serine, and threonine metabolism and tryptophan metabolism were clearly induced by F. fujikuroi, but different metabolites were mostly enriched in isoflavone biosynthesis, flavone biosynthesis, and galactose pathways. Interestingly, glycitein and glycitin were dramatically upregulated while daidzein, genistein, genistin, and daidzin were largely downregulated. These results indicate a combination of physiological responses, cell wall-related defense, and the complicated metabolites of soybean seeds contributes to soybean seed resistance against F. fujikuroi, which are useful for soybean resistance breeding.

Biological N fixation activity in soybean can be estimated based on nodule dry weight and is increased by additional inoculation

The response of soybean to biological nitrogen fixation (BNF) varies due to factors such as plant variety, soil, and environmental conditions, which can compromise the N2 fixation capacity. The objective of this study was to evaluate additional inoculation with B. japonicum at different soybean growth stages as a strategy to improve N2 fixation. This was determined by studying plant nodulation and BNF with the isotope dilution method. The experiment was carried out under greenhouse conditions in a soil without soybean cropping history. Inoculated soybean increased nodulation, plant biomass, BNF and yield components compared to un-inoculated soybean. Additional inoculation at the V3 growth stage showed promising results by maintaining BNF at later growth stages. A strong correlation was observed between nodule dry weight and N2 fixation, providing an easily available method to estimate differences in BNF activity between treatments. Moreover, efficient nodulation is crucial to sustain high yields with BNF, as shown by the correlation between nodule dry weight and pod dry weight.

The effects on the root and plant development of soybean of organic fertilizer applications

Organic fertilizers increase soil aeration, water and nutrient holding capacity, improve seed germination and encourage the production of thicker roots, increase the yield and quality of the crop. This study was aimed to determine the effects of organic fertilizer (vermicompost, sheep manure, cattle manure, poultry manure and inorganic fertilizer as control) applications on the root and plant development of soybean during two growth stages, namely third trifoliate stage (V3) and beginning of flowering of the plants (R1) in the pot condition. The experiment was established with a two-factorial randomized plot design with four replications. The fertilizer applications had a significant effect (p&lt;0.01) on the V3 and R1 growth stages of soybean. The highest plant height (36.78 cm) and root length (41.18 cm) were achieved with vermicompost, and the lowest plant height (21.73 cm) was recorded with inorganic fertilizer. The number of nodules was highest for vermicompost and cattle manure applications (17.16 plant-1 and 16.76 plant-1, respectively), and lowest for poultry manure (0.22 plant-1). In the R1 growth stage, poultry manure produced the highest biological fresh and dry weights, root dry weight at 25.08 g plant-1 and 5.67 g plant-1, 3.99 g plant-1 respectively. As a result, it was demonstrated that vermicompost and poultry manure applications promote strong root and plant development due to high organic matter and phosphorus and can be used successfully without inorganic fertilize application in soybean farming.

Effect of Rye cover crop on weed control, soybean (Glycine max L.) yield and profitability

Considerable variations in farm productivity were reported across soils and climates when winter cover crops (CC) were rotated with summer main cash crops. Hence, a three-year field study (2019-2021) was conducted on Dundee silt loam in a humid climate to assess soybean growth and yield, weed control, and profitability under no-till conditions in response to (i) no CC (NC), (ii) winter rye CC rolled when green, followed by soybean planting and desiccation by paraquat (GR) and iii) winter rye CC desiccated using paraquat and rolled followed by soybean planting (BR). No differences in phenological growth stages of soybean were observed among the treatments. Measured leaf area index was comparable among the treatments across the three seasons. The rate of rye CC biomass decay estimated eight weeks after planting (WAP) was much higher than at four WAP. In 2019, at eight WAP plant residue ranged from 29.3% under NC to 52.9% under GR, indicating the paraquat desiccated natural winter vegetation decays faster than the desiccated rye CC biomass. The weed biomass was 72% higher at eight WAP (0.29 Mg ha-1) than that of four WAP (0.17 Mg ha-1) and NC plots had higher weed biomass at both four WAP and eight WAP over CC plots. Field established soybean stand in the GR plots were consistently higher than the NC plots by 8%, 30%, and 22% in 2019, 2020, and 2021, respectively. Soybean yield in NC plot was 13% higher than GR and 15% higher than BR plots in 2019. However, in 2020 and 2021, soybean yield from BR and GR plots was significantly higher than NC plots (10% and 13%, respectively). In the three-year study, net returns from soybean with rye CC (regardless of GR or BR) in the first year was negative. In the second and third year, net returns in GR and BR were positive and comparable to NC. There were no differences in soybean yield and net returns between rye CC rolled green (GR) and rye CC desiccated (BR) prior to planting. These results show that a rye CC–based soybean conservation production system could be an economically a viable choice after the first year with an invaluable potential for carbon sequestration, weed suppression and positive impact on summer soybean productivity.

The impact of electrocution treatments on weed control and weed seed viability in soybean

AbstractField experiments were conducted in 2020 and 2021 to determine the effectiveness of electrocution on several weeds commonly encountered in Missouri soybean production using an implement known as The Weed Zapper™. In the first study, the effectiveness of electrocution on waterhemp, cocklebur, giant and common ragweed, horseweed, giant and yellow foxtail, and barnyardgrass was determined. Electrocution was applied when plants reached average heights and/or growth stages of 30 cm, 60 cm, flowering, pollination, and seed set. Electrocution was applied once or twice, at two different tractor speeds. Electrocution was more effective at the later plant growth stages. Pearson correlation coefficients indicated that control of weed species was most related to plant height and amount of plant moisture at the time of electrocution. When plants contained seed at the time of electrocution, viability was reduced from 54% to 80% among the species evaluated. A second study determined the effect of electrocution on late-season waterhemp plants, and also soybean injury and yield. Electrocution timings took place throughout reproductive soybean growth stages. The control of waterhemp escapes within the soybean trial ranged from 51% to 97%. Yield of soybean electrocuted at the R4 and R6 growth stages was similar to that of the nontreated control, but soybean yield was reduced by 11% to 26% following electrocution at all other timings. However, the visual injury and yield loss observed in these experiments likely represents a worst-case scenario because growers who maintain a clear height differential between waterhemp and the soybean canopy would not need to maintain contact with the soybean canopy. Overall, results from these experiments indicate that electrocution as part of an integrated weed-management program could eliminate late-season herbicide-resistant weed escapes in soybean, and reduce the number and viability of weed seed that return to the soil seedbank.

Soybean yield loss from delayed postemergence herbicide application based on weed height, days after emergence, accumulated crop heat units, and soybean growth stage

AbstractLimited information exists on the critical time of weed removal (CTWR) with the currently used soybean cultivars in Ontario. A study consisting of eight field experiments was conducted from 2017 to 2019 in Ontario, Canada, to determine the impact of delayed postemergence (POST) herbicide application on soybean yield based on average weed height at application, days after crop emergence (DAE), accumulated crop heat units (CHU) from the date of planting, and soybean growth stage. The regression model estimated the weed size at herbicide application that led to 1%, 2.5%, and 5% yield loss in soybean was 9, 14, and 20 cm under low weed density (averaging 73 to 134 plants m−2) and 3, 4, and 6 cm under high weed density (143 to 153 plants m−2) conditions, respectively. The estimated DAE at herbicide application time that led to 2.5%, 5%, 10%, and 25% yield loss in soybean was 24, 30, 37, and 53 DAE under low weed density and 8, 10, 14, and 23 DAE under high weed density, respectively. The predicted crop stage at herbicide application that resulted in 2.5%, 5%, 10%, and 25% yield loss in soybean was V4, V5, R2, and R5 under low weed density and VE, VC, V1, and V4 under high weed density, respectively. This study concludes that soybean yield loss is influenced by the weed density (low vs/high) and the time of the first POST herbicide application. When the first POST herbicide application was delayed until soybean was at the V2 stage the monetary loss was Can$20.46 and Can$221.20 ha−1 in low and high weed-density environments, respectively.

Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data

Accurate and timely crop phenology is essential for efficient crop management and precise modelling of crop yield and productivity. It has been commonly mapped from moderate or coarse resolution (≥500 m) satellite data at a national or global scale, which generally represents the mixture of multiple crops or between crops and natural vegetation. Crop phenological progress at field scales (≤30 m) is critical for monitoring the growth of specie-specific crop types, but it is very challenging at large scales because of limited cloud-free observations in the satellite time series. To map corn and soybean phenometrics at a field scale (30 m) over entire Corn Belt in the United States, this study for the first time develops a new approach that fuses high temporal resolution Visible Infrared Imaging Radiometer Suite (VIIRS) observations (500 m) and high spatial resolution Harmonized Landsat and Sentinel-2 (HLS) data (30 m). The fused HLS-VIIRS time series (daily 30 m) is applied to map crop phenometrics of greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. These six phenometrics are correlated to crop progress (CP) of seven corn and six soybean growth stages from planting to harvest that are observed by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) and are further validated using PhenoCam near-surface time series imagery. The result indicates that the HLS-VIIRS crop phenometrics are capable of tracking the CP very well. Although their biophysical definitions of CP stages and sample coverage were not the same in our comparisons, the HLS-VIIRS crop phenometrics and NASS CP at a state level are closely comparable with a difference<4 days for corn and soybean emergence, 1 day for soybean dropping leaves, and 6–10 days for other corn and soybean growth stages. Their significant correlations (R2 ranging from 0.81 to 0.99, p < 0.01) suggest that the HLS-VIIRS crop phenometrics are robust proxies for predicting NASS CP with a high accuracy. Moreover, the HLS-VIIRS phenometrics aligns closely with PhenoCam phenological timings at various crop growth stages with an absolute average difference from 3 days to 5.3 days and bias from 1.5 to 3.5 days. The result demonstrates that the proposed approach could be implemented for monitoring field scale crop progress at a national or global scale.

Características agronômicas de cultivares de soja com aplicação tardia de nitrogênio em suplementação à inoculação de Bradyrhizobium spp.

ABSTRACT Late season nitrogen (N) applications may serve as cultural practices to increase soybean yield and grain protein concentration in modern high-yielding cultivars. A study to evaluate the response of soybean cultivars to late N application in supplementation to Bradyrhizobium spp. inoculation was conducted in two Brazilian Cerrado agricultural soils with medium and high fertility during the 2019/2020 growing season. Treatments were arranged in a randomized block design in a 2×2×5 factorial scheme: two agricultural production environments [UFMS 1 (medium fertility) and UFMS 2 (high fertility)], two soybean cultivars (TMG 7067 IPRO and BMX Bônus IPRO), and five late N application rates (0, 50, 100, 150 and 200 kg N ha-1), with four replicates. Canonical correlation analysis (CCA) was used to investigate the interrelationships between the groups of independent (agricultural production environments, soybean cultivars, and N application rates) and dependent (soybean agronomic traits) variables. Nitrogen rates were applied in topdressing at the R5.3 soybean growth stage. Late N applications did not affect soybean agronomic traits (plant height, first pod height, pod number per plant, grain number per pod, and 1,000-grain mass) and did not increase the grain protein concentration or yield of the soybean cultivars, regardless of the fertility level of the agricultural area. We conclude that when efficient strains of Bradyrhizobium spp. are used in soybean cropping in medium- and high-fertility Cerrado soils, there is no need to apply late rates of N fertilizer.

The Impact of Fungicide Application Method on Soybean Canopy Coverage, Frogeye Leaf Spot, and Yield

Field trials were conducted across Iowa from 2017 to 2020 to investigate the effect of fungicide application methods (traditional and undercover) on coverage, disease severity, and yield. Foliar fungicides were applied at the soybean (R3) growth stage by a traditional ground sprayer with an overhead spray boom or a ground sprayer with 360 undercover sprayers that hang down into the canopy. Fungicide coverage was measured as milliliters per liter of pyrenetetrasulfonic acid dye detected on soybean leaves and as a percent coverage of water-sensitive spray cards from three canopy zones (upper, middle, and lower). Both detection methods showed that the upper canopy received significantly more coverage through the traditional sprayer than the undercover sprayer. The percent coverage in the middle canopy was not different between the two application methods. The tracer dye detected greater fungicide distribution in the lower canopy by the undercover than the traditional method ( P = 0.034). Greater disease severity was observed in 2018 and 2019 than in 2017 and 2020. Regardless of the year or application method, no location in the canopy had a significant difference in foliar disease control, nor did fungicide application affect yield. Results from these trials show adequate coverage of fungicide, but disease severity was not reduced, and yield was not improved.

Counting the nodules that count, relationships between seed nitrogen and root nodules

AbstractThe nodules on the roots of soybeans provide N that the plant uses for growth and development. The goal of this study is to observe if the number of nodules on the taproot, volume of nodules on the taproot, the total number of nodules, or the number of nodules on the secondary roots in the early growth stages of soybeans have a significant correlation with the amount of elemental N in mature seed. The Soybean Nodule Acquisition Pipeline was used to quantify these traits and the mature seed was analyzed to obtain the amount of elemental N. The data set used for this project consisted of six genetically and phenotypically diverse soybean lines from two unique sites in 2018. Strong positive correlations were discovered between the number and volume of nodules on the taproot and the amount of N in mature seed. There was a less significant correlation between total nodule counts and N in mature seeds. There was not a strong significant correlation between nodules on the secondary roots and N in mature seeds.

Soybean Roots and Soil From High- and Low-Yielding Field Sites Have Different Microbiome Composition.

The occurrence of high- (H) and low- (L) yielding field sites within a farm is a commonly observed phenomenon in soybean cultivation. Site topography, soil physical and chemical attributes, and soil/root-associated microbial composition can contribute to this phenomenon. In order to better understand the microbial dynamics associated with each site type (H/L), we collected bulk soil (BS), rhizosphere soil (RS), and soybean root (R) samples from historically high and low yield sites across eight Pennsylvania farms at V1 (first trifoliate) and R8 (maturity) soybean growth stages (SGS). We extracted DNA extracted from collected samples and performed high-throughput sequencing of PCR amplicons from both the fungal ITS and prokaryotic 16S rRNA gene regions. Sequences were then grouped into amplicon sequence variants (ASVs) and subjected to network analysis. Based on both ITS and 16S rRNA gene data, a greater network size and edges were observed for all sample types from H-sites compared to L-sites at both SGS. Network analysis suggested that the number of potential microbial interactions/associations were greater in samples from H-sites compared to L-sites. Diversity analyses indicated that site-type was not a main driver of alpha and beta diversity in soybean-associated microbial communities. L-sites contained a greater percentage of fungal phytopathogens (ex: Fusarium, Macrophomina, Septoria), while H-sites contained a greater percentage of mycoparasitic (ex: Trichoderma) and entomopathogenic (ex: Metarhizium) fungal genera. Furthermore, roots from H-sites possessed a greater percentage of Bradyrhizobium and genera known to contain plant growth promoting bacteria (ex: Flavobacterium, Duganella). Overall, our results revealed that there were differences in microbial composition in soil and roots from H- and L-sites across a variety of soybean farms. Based on our findings, we hypothesize that differences in microbial composition could have a causative relationship with observed within-farm variability in soybean yield.