Soybean Growth Stages Research Articles

The effectiveness of herbicidal desiccants and application times on seed yield and earliness of soybean

Two herbicidal desiccants and five application times at different reproductive stages of soybean were investigated for earliness without significant reduction in seed yield and quality. Glyphosate and diquat were applied to soybean cv. ‘Arısoy’ (maturity group III) at growth stages of R5, R6, R7 and R8. Full maturity (FM) and uprooted plants at each growth stage were also used as a control. Plant height, first pod height, pod number per plant, seed weight per plant, thousand seed weight, seed yield and oil ratio were investigated. The results showed that plant height was not affected by desiccants and application times. Lower seed weight per plant, thousand seed weight, seed yield, and oil ratio were obtained from earlier reproductive stages, especially at R5, and R6, but glyphosate produced higher seed yield and oil content than diquat. Both desiccants considerably reduced seed yield when applied at R5 and R6. Mean seed yield enhanced from 1,364 to 3,036 kg ha-1 in first year and from 1,097 to 2,804 kg ha-1 in second year as the growth stages extended from R5 to R8. Diquat was more effective desiccant for accelerating soybean harvest than glyphosate, while higher seed yield was obtained from glyphosate spraying at R5 and R6 stages. Increased seed yield was observed in diquat application when the soybean growth stages were delayed from R5 to R8. It was concluded that glyphosate should be applied at R7 in soybean for earliness with 7-10 days in harvest and no desiccant should be suggested before R7 stages.

A Radar Vegetation Index for Crop Monitoring Using Compact Polarimetric SAR Data

Crop growth monitoring using compact-pol synthetic aperture radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this article, we propose a vegetation index for compact polarimetric (CP) SAR data [compact-pol radar vegetation index (CpRVI)]. The CpRVI is derived using the concept of a geodesic distance between the Kennaugh matrices projected on a unit sphere. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an ideal depolarizer (a realization of vegetation canopy). The similarity measure is then modulated with a scaled quantity derived from the scattering power ratio of the same and opposite sense polarization with respect to the transmitted circular polarization. In this article, we utilize time-series-simulated RADARSAT Constellation Mission (RCM) compact-pol SAR data (RH-RV) obtained from the full-pol RADARSAT-2 observations during the soil moisture active passive (SMAP) validation experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, to assess the proposed vegetation index. Among the various crops grown in this region, in particular, we analyze the growth stages of wheat and soybean due to their different canopy structures. A temporal analysis of the proposed CpRVI with crop biophysical parameters [the plant area index (PAI) and vegetation water content (VWC)] at different phenological stages confirms the trend of CpRVI with the plant growth. Nevertheless, variations of CpRVI values are apparent with different plant densities for both the crop types. Also, the linear regression analysis confirms that the CpRVI values significantly correlate with PAI (r = 0.72 and 0.85) and VWC (r = 0.62 and 0.75) for both wheat and soybean. We observed good retrieval of PAI and VWC for both wheat and soybean.

Heat Storage Capacity and Temporal-spatial Response in the Soil Temperature of Albic Soil Amended with Maize-derived Biochar for 2 Years

Under cold climate conditions, the temporal and spatial distribution of heat and the influencing factors in different layers of Albic soil remain unclear. Here, we aimed to clarify the influence of biochar on soil heat storage capacity and identify approaches to regulate temporal-spatial variations in Albic soil temperature. To this end, we studied thermal properties and temporal-spatial temperature variations in different soil layers (10 and 20 cm) amended with biochar at different concentrations (0, 10, 20, 30, 40 g·kg-1) in the key growth stages of soybeans through pot experiments for two years. We found that the daily mean soil temperature and accumulated soil temperature improved during the 2 years in both soil layers after biochar application. Compared with the control, the biochar treatments increased soil daily minimum temperature, decreased soil daily maximum temperature and soil temperature fluctuations, postponed the soil heating stage by 1 h, and caused a "warming effect" during the soil cooling stage. Furthermore, biochar treatment enhanced the thermal capacity of the Albic soil by 7.49% and 6.53% compared with the control in 2016 and 2017, respectively, whereas thermal conductivity and thermal diffusivity were decreased compared with the control. Biochar regulated the temporal-spatial response variation in temperature of the Albic soil as follows: biochar distributed heat in the Albic soil more uniformly over time by changing the soil temperature cycle and reducing the temperature difference, and heat tended to move downwards into the soil layers by increasing the heat capacity and reducing soil thermal conductivity and thermal diffusivity. Biochar changed the heat storage capacity of the Albic soil and temporal-spatial distribution of heat. These changes provide sufficient, timely, and effective heat supply for crop growth. Furthermore, biochar treatments had a significant and positive effect on soil physical properties and crop yield. In cold areas, these changes would help crops resist temperature changes caused by extreme weather and reduce the rate of cold damage. The results of this study have important practical relevance for enriching the basic scientific theory of biochar application.

Nitrogen Fertilizer Recovery and Partitioning Related to Soybean Yield

This study checked the nitrogen (N) uptake, fate of the N fertilizer, fertilizer recovery efficiency (FRE) and the residual amount of N fertilizer in the soil via 15N-labelled fertilizer applied to soybean (Glycine max L. Merr.), to explain any differences in the yields. Two soybean field experiments were established in Brazil, one conducted in a tropical (Trop) zone located at Cerrado Biome and the other in a subtropical (Subt) environment. The experimental design was a 2-factor in randomized complete block with four replications. Five doses of N (0, 20, 40, 80 and 120 kg ha−1) were applied at two soybean growth stages (VE and R3). For all treatments, except No-N, 15N-labelled fertilizer was used. The N uptake and the amount of N fertilizer were analysed in the roots, shoots, grains and whole plant, the FRE and yield by soybean, and the residual of fertilizer in the soil. The total N uptake (Nplant) was greater with fertilization at R3 stage compared to the other stage, and N application increased the yield just at this stage and in the Trop condition. The increase in N shoot (not specifically from fertilizer) with the application at R3 appeared to be related to the increased in yield. However, the N from fertilizer found in the plant shoot was about 20% higher when fertilizer was applied at VE compared with R3. Under Subt condition, the FRE averaging 55%. In contrast, the FRE decreased from low to high nitrogen rates (64 to 40%, respectively) when soybean grown under Trop condition. Most N from fertilizer (38%) was found in the grains, followed by the shoot (14.6%) and the root (0.58%). The use of N fertilizer at reproductive growth stages is a better approach to meet soybean N demand through N fertilization. Potential yield gains are more reliable under Trop condition.

The influence of soybean population and POST herbicide application timing on in-season and subsequent-season Palmer amaranth (Amaranthus palmeri) control and economic returns

AbstractOverreliance on herbicides for weed control has led to the evolution of herbicide-resistant Palmer amaranth populations. Farm managers should consider the long-term consequences of their short-term management decisions, especially when considering the soil weed seedbank. The objectives of this research were to (1) determine how soybean population and POST herbicide application timing affects in-season Palmer amaranth control and soybean yield, and (2) how those variables influence Palmer amaranth densities and cotton yields the following season. Soybeans were planted (19-cm row spacing) at a low-, medium-, and high-density population (268,000, 546,000, and 778,000 plants ha–1, respectively). Fomesafen and clethodim (280 and 210 g ai ha–1, respectively) were applied at the VE, V1, or V2 to V3 soybean growth stage. Nontreated plots were also included to assess the effect of soybean population alone. The following season, cotton was planted into these plots so as to understand the effects of soybean planting population on Palmer amaranth densities in the subsequent crop. When an herbicide application occurred at the V1 or V2 to V3 soybean stage, weed control in the high-density soybean population increased 17% to 23% compared to the low-density population. Economic return was not influenced by soybean population and was increased 72% to 94% with herbicide application compared to no treatment. In the subsequent cotton crop, Palmer amaranth densities were 24% to 39% lower 3 wk after planting when following soybean sprayed with herbicides compared to soybean without herbicides. Additionally, Palmer amaranth densities in cotton were 19% lower when soybean was treated at the VE stage compared to later stages. Thus, increasing soybean population can improve Palmer amaranth control without adversely affecting economic returns and can reduce future weed densities. Reducing the weed seedbank and selection pressure from herbicides are critical in mitigating resistance evolution.

Row spacing, cultivar and fungicide application affect the incidence of soybean stem blight

Brazil is a major soybean production country and cultivate yearly more than 36 million hectares. Several factors have been reported reducing soybean production and occurrence of diseases is one of the most important. Some diseases such as the pod and stem blight (Diaporthe phaseolorum var. sojae) can limit soybean yield by shortening plant cycle and reducing seed quality. Field experiments were conducted aiming to evaluate the effect of row spacing, soybean cultivar and fungicide application on the incidence of pod and stem blight on soybean. The trial was carried out twice, with planting date at 11/15/2012 and 12/15/2012, in Planaltina-DF, Brazil. Treatments were arranged in a split-split plot scheme obeying the randomized block design. The factors evaluated were row spacing (42, 60 and 75 cm), soybean cultivars (“Syn1180RR” and “Syn1080RR”) and spray or not the fungicide Boscalid (500 g a.i./ha) at R1 and R3 soybean growth stages. Disease incidence was assessed on stems at the harvest time and on seeds after harvest and yield was estimated. Results obtained show that broader row spacing increased disease incidence on stems and seed, cultivar “Syn1180RR” was more susceptible than “Syn1080RR” and fungicide application reduced disease incidence. Pod and stem blight significantly reduced soybean yield.

PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans

The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.

A LAMP-assay-based specific microbiota analysis reveals community dynamics and potential interactions of 13 major soybean root pathogens

Soybean root diseases are associated with numerous fungal and oomycete pathogens; however, the community dynamics and interactions of these pathogens are largely unknown. We performed 13 loop-mediated isothermal amplification (LAMP) assays that targeted specific soybean root pathogens, and traditional isolation assays. A total of 159 samples were collected from three locations in the Huang-Huai-Hai region of China at three soybean growth stages (30, 60, and 90 days after planting) in 2016. In LAMP results, we found that pathogen communities differed slightly among locations, but changed dramatically between soybean growth stages. Phytophthora sojae, Rhizoctonia solani, and Fusarium oxysporum were most frequently detected at the early stage, whereas Phomopsis longicolla, Fusarium equiseti, and Fusarium virguliforme were most common in the later stages. Most samples (86%) contained two to six pathogen species. Interestingly, the less detectable species tended to exist in the samples containing more detected species, and some pathogens preferentially co-occurred in diseased tissue, including P. sojae–R. solani–F. oxysporum and F. virguliforme–Calonectria ilicicola, implying potential interactions during infection. The LAMP detection results were confirmed by traditional isolation methods. The isolated strains exhibited different virulence to soybean, further implying a beneficial interaction among some pathogens.

Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images

Iron deficiency chlorosis (IDC) is a major yield-limiting factor for soybean production in the mid-western USA. The most practical solution in mitigating losses due to IDC is the development and characterization of IDC tolerant varieties. Leveraging the advanced technique of unmanned aircraft system (UAS) and the thriving deep learning methodology, a convolutional neural network (CNN) could be trained to assist breeders with IDC resistance selection. However, a known difficulty in IDC screening is that the symptoms often vary across diverse genetic backgrounds and spatial or temporal soil heterogeneities. A robust CNN model is desired to mitigate such difficulty. While high robustness usually relies on a sufficiently large labeled training data, the available labeled samples in most breeding programs are normally not enough. Under this limitation, it is critical to find an alternative way to train a robust model. The solution proposed in this study was to apply unsupervised pre-training on the unlabeled aerial images that are much easier to obtain by the UAS. Specifically, a convolutional autoencoder (CAE) was pre-trained on unlabeled sub-images clipped from aerial RGB images; then, the pre-trained weights were reused to initialize the CNN model that was trained on labeled plot-wise sub-images clipped from stitched RGB maps. To test the robustness of this CAE initialized model (CAE1-CNN), two baseline models were equally trained: the first was CAE2-CNN, where the CAE2 was pre-trained with three times of unlabeled data as that of CAE1, by adding wniter wheat and sorghum aerial images; the second was Ran-CNN where the CNN was randomly initialized. Three conditions were considered for testing model robustness: different soybean trials, field locations and vegetative growth stages. Results revealed that both the CAE1-CNN and the CAE2-CNN had relatively better robustness than the Ran-CNN model, i.e., higher R2 and lower RMSE values, especially on different soybean trials and growth stages, which proved that the unsupervised pre-training added gains to the model robustness across diverse trials and growth stages. Similar performances were found between the CAE1-CNN andthe CAE2-CNN model, suggesting that augmenting the unlabled data did not bring significant improvement to model robustness. Additionally, during robustness test on different soybean trials, the unsupervised pre-training seemly showed the potential of alleviating the required number of labeled training samples. These promising findings could contribute to the research on crop stresses by providing a potential path towards developing a robust system for classifying or predicting stress severities under more varied conditions.

Location of Helicoverpa zea (Lepidoptera: Noctuidae) larvae on different plant parts of determinate and indeterminate soybean.

Helicoverpa zea (Boddie) is a damaging pest of many crops including soybean, Glycine max (L.), especially in the southern United States. Previous studies have concluded that oviposition and development of H. zea larvae mirror the phenology of soybean, with oviposition occurring during full bloom, younger larvae developing on blooms and leaves, intermediate aged larvae developing on varying tissue types, and older larvae developing on flowers and pods. In a field trial, we investigated the presence of natural infestations of H. zea larvae by instar in determinate and indeterminate soybean varieties. In complementary experiments, we artificially infested H. zea and allowed them to oviposit on plants within replicated cages (one with a determinate variety and two with an indeterminate variety). Plants were sampled weekly during the time larvae were present. In the natural infestation experiment, most larvae were found on blooms during R3 and were early to middle instars; by R4, most larvae were found on leaves and were middle to late instars. In contrast, in the cage study, most larvae were found on leaves regardless of soybean growth stage or larval stage. Determinate and indeterminate growth habit did not impact larval preference for different soybean tissue types. Our studies suggest H. zea larvae prefer specific tissue types, but also provide evidence that experimental design can influence the results. Finally, our finding of larval preference for leaves contrasts with findings from previous studies.

Modelling the impacts of climate change and crop management measures on soybean phenology in China

Climate change and crop management measures affect crop growth. But their combined and relative impacts on soybean phenology are still not clear. Based on the first-difference method, we discriminated and quantified the impacts of climate change and crop management measures on soybean phenology using observational data from 51 agro-meteorological stations in China’s major soybean planting areas from 1992 to 2011. Results showed that (1) vegetative, reproductive and whole growth periods of northern spring soybean were shortened whereas those of summer soybean were prolonged. Reproductive and whole growth periods of southern spring soybean were also prolonged. (2) Climate change independently prolonged the vegetative growth periods of northern spring soybean, summer soybean, and southern spring soybean by 0.3, 0.52, and 0.3 days/decade, respectively, and shortened the whole growth periods by 1.43, 0.74, and 6.3 days/decade, respectively. Increasing temperature advanced the soybean phenology and shortened key growth stages. (3) Crop management measures contributed more to the observed trends of phenology and growth stages than climate change. Temperature and sunshine duration dominated the impact of climate change on soybean phenology and growth stages. (4) Shifting sowing date and selecting soybean cultivars with high temperature resistance, high thermal requirements and long growth periods are effective strategies to adapt to climate change. The findings could contribute to providing appropriate adaptation and mitigation strategies to facilitate sustainable soybean production.

A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery

Crop emergence date is a critical input to models of crop development and biomass accumulation. The ability to robustly detect and map emergence date using remote sensing would greatly benefit operational yield estimation and crop monitoring efforts; however, this has proven to be challenging. Previous remote-sensing phenology algorithms showed that crop stages can typically be detected starting only around the V3-V4 (3 to 4 established leaves) vegetative stage. Furthermore, traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for within-season operational mapping, particularly in the early growing season. In the current paper, we describe a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations available during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2-day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENμS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote-sensing green-up dates were about 4–5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5-day revisit) over BARC. The detected green-up dates from Sentinel-2 were consistent with those from VENμS. Some fields were not detected due to the lack of observations around the emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENμS and the Harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3–4-day revisit). The detected green-up dates were compared with emergence dates in crop progress reports from the National Agricultural Statistics Service (NASS) at the state-level. The WISE-detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote-sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.

Methane consumption potential of soybean-wheat, maize-wheat and maize-gram cropping systems under conventional and no-tillage agriculture in a tropical vertisol

AbstractMethane (CH4) consumption in agricultural soil is imperative for the mitigation of climate change. However, the effect of tillage and cropping systems on CH4consumption is less studied. Experiments were carried out in Madhya Pradesh, India with soybean-wheat (SW), maize-wheat (MW) and maize-gram (MG) cropping systems under conventional tillage (CT) and no-tillage (NT). Soybean/maize was cultivated during thekharifseason (July–October) and wheat/chickpea in therabiseason (October–March) for 9 years consecutively. Soil samples were collected during vegetative growth stages of soybean and maize from different cropping systems. Methane consumption, the abundance of methanotrophs as particulate methane monooxygenase (pmoA) gene copies, soil and crop parameters were estimated. Methane consumption rate was higher in NT and upper soil layer (0–5 cm) than CT and 5–15 cm depth. Methane consumption rate k ranged from 0.35 to 0.56 μg CH4consumed/g soil/d in the order of MW>SW>MG in 0–5 cm. The abundance ofpmoAgene copies ranged from 43 × 104/g soil to 13 × 104/g soil and was highest in MW-NT and lowest in MG-CT. Available nitrogen, phosphorus and potassium were higher in 0–5 cm than in 5–15 cm depth. Soil and plant parameters and abundance ofpmoAgenes correlated significantly and positively with CH4consumption rate. No-tillage stimulated CH4consumption compared to CT irrespective of cropping system and CH4consumption potential was highest in MW and lowest in MG. However, the magnitude of the positive effect of NT towards CH4consumption was higher in SW and MG than MW.

Uticaj pre-em primene herbicida na kritično vreme suzbijanja korova u soji tolerantnoj na dikambu

Field experiment was conducted in 2019 at Haskell Agriculture Laboratory, Concord, NE, USA. Goal of the study was to test the influence of PRE-EM herbicides on the Critical Time for Weed Removal (CTWR) in dicamba-tolerant soybean. The study was arranged in a split-plot design which consisted of four herbicide regimes as main plot treatments and seven weed removal timings as subplot treatments, with four replications. The herbicide regimes included: (1) no PRE and glyphosate, (2) acetochlor and dicamba as PRE and glyphosate as POST, (3) acetochlor and dicamba as PRE and glyphosate and dicamba as POST, and (4) acetochlor and fomesafen as PRE and acetochlor, glyphosate and dicamba as POST. The five weed removal times included the V1, V3, V6, R2 and R5, and there were also weedy and weed-free season long plots. By utilizing herbicide regimes, the CTWR was delayed to 632 GDD (until V4 soybean growth stage, 28 days after emergence) for acetochlor and dicamba as PRE and glyphosate as POST, 861 GDD (until V6 soybean growth stage, 32 days after emergence) for acetochlor and dicamba as PRE and glyphosate and dicamba as POST, and 1060 GDD (until R1 soybean growth stage, 42 days after emergence) for acetochlor and fomesafen as PRE and acetochlor, glyphosate and dicamba as POST.

An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

Relationship between soybean yield from high and low yielding field sites and selected soil characteristics

AbstractThe sporadically occurring high and low yielding field sites is a commonly observed manifestation of the within‐farm spatial heterogeneity of soybean [Glycine max (L.) Merr.] yield in Pennsylvania. Understanding the causes behind this phenomenon is important for implementing site‐specific management practices, leading to greater attainable yields at farm scale. To explore the problem, bulk soil samples were systematically collected in 2018 from farmer‐designated high and low yielding sites at V1 (early season) and R8 (late season) soybean growth stages from 14 farms in Pennsylvania. Soil organic matter; cation exchange capacity; pH; P, K, Mg, Ca, S, Zn, and Cu; soil texture; site slope; number of putatively pathogenic fungal colonies (Fusarium, Pythium, and Phytophthora species and Rhizoctonia solani); and plant pathogenic nematodes were assayed. No measured variables differed significantly between two site types at V1 or R8. Principal component analysis did not distinctly cluster high and low sites based on the variables included in the analysis. Hierarchical clustering on principal components showed situations where sites from different farms are more similar than sites from the same farm. Correlation analysis performed for variables collected with high sites showed that yield is significantly and positively correlated with V1‐spiral nematode count, R8‐R. solani colony count, and soil K content at V1 and R8. For low sites, yield was significantly and positively correlated with V1‐spiral nematode count and soil P/Zn content at R8. Taken together, our findings indicate that the causes behind within‐farm spatial heterogeneity of soybean yields are complex and beyond the variables investigated in this study.

Soybean Nodulation and Plant Response to Nitrogen and Sulfur Fertilization in the Northern US

Soybean [Glycine max (L.) Merrill] seed yields in the northern United States may increase with the application of fertilizers; however Nitrogen (N) may decrease root nodulation. This study was conducted to understand the impact of N and sulfur (S) fertilization on soybean nodulation, plant, shoot and root biomass. Two cultivars were planted in experiments across ten site-years during 2015-2016. Plant observations took place at the V4 and R4 soybean growth stages. There were 41% more nodules per plant at R4 compared to V4 (38.3 vs 27.2 nodules, respectively). Cultivars responded differently to N and S fertilizer. The nodules per plant between the cultivars (30.3 vs 24.4) were different as well as the percent medium and large-sized nodules, which indicates the need to evaluate additional genotypes. Adding N decreased root nodulation (from 31.8 to 23.7 nodules per plant) and decreased nodule size but had no effect on plant, shoot or root mass. Averaged across N rates total plant mass was 2.26 and 11.36 g per plant at V4 and R4, respectively. Shoot mass, average across N rates was 1.77 and 9.65 g per plant at V4 and R4, respectively, and root mass, average across N rates was 0.49 and 1.71 g per plant at V4 and R4, respectively. Sulfur did not have an effect on nodules per plant but increased the percent medium size nodules at the R4 observation. There was no N by S interaction observed for nodule number, size of the nodules, and plant, root and shoot mass. As cultivars differed in their nodulation response to N and S, additional research would be helpful to screen other cultivars.

Physiological attributes of Enlist E3™ soybean seed submitted to herbicides application

Background: Enlist E3™ soybean is tolerant to 2,4-D, glyphosate and ammonium-glufosinate and were developed for effective management of herbicide resistant weeds such as glyphosate Herbicides, even being selective, can cause damage to crop metabolism, affect growth and development and interfere with seed physiological quality. Objective: The aim of this research was to evaluate the physiological quality of Enlist E3™ soybean seeds submitted to herbicide application. Methods: The field trial was carried out in a randomized block design with four replications. The treatments tested were 2,4-D choline + glyphosate, 2,4-D choline, glyphosate + 2,4-D amine and ammonium-glufosinate; and control without application, sprayed at V6 soybean growth stage. After harvest, the seeds were submitted to first germination count, accelerated aging, germination speed index, shoot and root length, shoot and root dry mass and electrical conductivity tests. Results: The management of Enlist E3 soybeans with Enlist herbicides (2,4-D choline + glyphosate and 2,4-D choline) as well as glyphosate + 2,4-D amine and glufosinate did not negatively impact soybean parameters such as: vigor, germination count, accelerated aging, germination speed, aerial part and primary root length, aerial part and root dry mass and electrical conductivity. Conclusions: Those herbicides can be recommended to be used on post emergence (V6) of Enlist E3 soybean.

Subdoses of 2,4-D Choline Salt on Yield Components in Function of the Soybean Growth Stage

Residues of herbicide tanks may cause damage to sensitive crops. The objective of this study is to evaluate the potential effects of subdoses of the herbicide 2,4-D choline salt on the vegetative and reproductive stages of soybean. The experiment was randomized blocks with a subdivided plot design and four replications. The application times were at V3 and R2 in the main plot. The herbicide 2,4-D choline salt was applied at the subdoses 0, 2.28, 4.56, 9.12, 18.24, 27.36 and 45.60 g a.e. ha-1 in the subplots. Phytotoxicity was evaluated at 3, 7 and 14 days after application (DAT) of treatments, soybean height at 14 and 28 DAT and at ripening, yield components at ripening, and grain yield. The phytotoxicity evaluation of 2,4-D choline salt evidenced that symptoms were only observed up to 14 days after application (DAT) of treatments. The 2,4-D did not affect any of the variables tested. Grain yield was not influenced by application times or subdoses.

The critical weed-free period in glyphosate-resistant soybean in Ontario is similar to previous estimates using glyphosate-susceptible soybean

A study consisting of 13 field experiments was conducted during 2014–2016 in southwestern Ontario and southcentral Nebraska (Clay Center) to determine the effect of late-emerging weeds on the yield of glyphosate-resistant soybean. Soybean was maintained weed-free with glyphosate (900 g ae ha−1) up to the VC (cotyledon), V1 (first trifoliate), V2 (second trifoliate), V3 (third trifoliate), V4 (fourth trifoliate), and R1 (beginning of flowering) growth stages, after which weeds were allowed to naturally infest the soybean plots. The total weed density was reduced to 24%, 63%, 67%, 72%, 76%, and 92% in Environment 1 (Exeter, Harrow, and Ridgetown) when soybean was maintained weed-free up to the VC, V1, V2, V3, V4, and R1 soybean growth stages, respectively. The total weed biomass was reduced by 33%, 82%, 95%, 97%, 97%, and 100% in Environment 1 (Exeter, Harrow, and Ridgetown) and 28%, 100%, 100%, 100%, 100%, and 100% in Environment 2 (Clay Center) when soybean was maintained weed-free up to the VC, V1, V2, V3, V4, and R1 stages, respectively. The critical weed-free periods for a 2.5%, 5%, and 10% yield loss in soybean were the V1–V2, VC–V1, and VC–V1 soybean stages in Environment 1 (Exeter, Harrow, and Ridgetown) and V2–V3, V2–V3, and V1–V2 soybean stages in Environment 2 (Clay Center), respectively. For the weed species evaluated, there was a minimal reduction in weed biomass (5% or less) when soybean was maintained weed-free beyond the V3 soybean growth stage. These results shows that soybean must be maintained weed-free up to the V3 growth stage to minimize yield loss due to weed interference.