V3 Growth Stage Research Articles

Stomatal Density Variation Within and Among Different Soybean Cultivars Across Various Growth Stages

Stomata regulate CO2 and water vapor exchange between leaves and the atmosphere, serving as a vital indicator of climate change resilience. Therefore, understanding the difference in stomatal numbers and patterns among different soybean cultivars across growth stages is essential to comprehending the complex mechanisms underlying soybean adaptation to climate change. The accurate measurements of stomatal density in soybean leaves are essential to understanding the complexity of stomatal density by environmental conditions. We demonstrated that the five epidermal sections and five microscopic images taken from both sides of each epidermal section at each leaf position (tip, middle, and bottom) were sufficient for stomatal measurements. Furthermore, we investigated variations in stomatal density among leaflet locations (left, right, and central) and leaf position across different growth stages. Notably, while there was no significant variation between the two leaves of the vegetative cotyledon (VC) stage and among the three leaflets of the V1 (first trifoliate) to V4 (fourth trifoliate) growth stages, leaves of the VC stage exhibited the lowest stomatal density, whereas those of the V4 stage exhibited the highest stomatal density. These findings could serve as a valuable tool for evaluating stomatal density, analyzing physiological differences under adverse climatic conditions, and phenotyping a large-scale population to identify the genetic factors responsible for stomatal density variations in soybean genotypes.

Advancing nitrogen nutrition index estimation in summer maize using continuous wavelet transform

Rapid and non-destructive diagnosis of plant nitrogen (N) status is crucial to optimize N management during the growth of summer maize. This study aimed to evaluate the effectiveness of continuous wavelet analysis (CWA) in estimating the nitrogen nutrition index (NNI), to determine the most suitable wavelet analysis method, and to identify the most sensitive wavelet features across the visible to near-infrared spectrum (325–1,025 nm) for accurate NNI estimation. Field experiments were conducted across two sites (Kaifeng and Weishi) during the 2022 and 2023 growing seasons using four summer maize cultivars (XD20, ZD958, DH661, and DH605) under varying N application rates (0, 80, 160, 240, and 320 kg N ha-1). Canopy reflectance spectra and plant samples were collected from the V6 to V12 growth stages. The wavelet features for each spectral band were calculated across different scales using the CWA method, and their relationships with NNI, plant dry matter (PDM), and plant N concentration (PNC) were analyzed using four regression models. The results showed that NNI varied from 0.61 to 1.19 across different N treatments during the V6 to V12 stages, and the Mexican Hat wavelet was identified as the most suitable mother wavelet, achieving an R² value of 0.73 for NNI assessment. The wavelet features derived from the Mexican Hat wavelet were effective in estimating NNI, PDM, and PNC under varying N treatments, with the most sensitive wavelet features identified as 745 nm at scale 7 for NNI, 819 nm at scale 5 for PDM, and 581 nm at scale 6 for PNC using linear regression models. The direct and indirect methods for NNI estimation were compared using independent field data sets. Both methods proved valid to predict NNI in summer maize, with relative root mean square errors of 10.8% for the direct method and 13.4% for the indirect method. The wavelet feature at 745 nm, scale 7, from the direct method (NNI = 0.14 WF (745 nm, 7) + 0.3) was found to be simpler and more accurate for NNI calculation. These findings provide new insights into the application of the CWA method for precise spectral estimation of plant N status in summer maize.

Novel Strain Bacillus velezensis LAFUEL 03: Activity Against Xanthomonas vasicola pv. vasculorum, Control of Bacterial Leaf Streak of Corn and Genome Insights into Its Antagonistic Activity

The main objective of this study was to investigate the antimicrobial activity of three putative antagonist bacterial strains of Bacillus spp. against Xanthomonas vasicola pv. vasculorum (Xvv) and their potential to control bacterial leaf streak (BLS) of corn. Additionally, the study included investigations on the genome of one of these antagonist bacteria, such as genome sequencing and mining of genes involved in biofilm formation, swarming motility, and synthesis of secondary metabolites. The growth of Xvv was inhibited by both cell suspensions and cell-free supernatants of the bacterial strains LAFUEL 01, LAFUEL 02, and LAFUEL 03 in agar diffusion tests. All three antagonist strains significantly reduced the severity of BLS in the 3rd and 4th leaves of corn plants that were artificially inoculated at the V3 growth stage under greenhouse conditions. The 16S rRNA sequencing confirmed that the antagonistic bacterial strains belong to the genus Bacillus, with LAFUEL 03 having approximately 97% similarity to B. velezensis. B. velezensis LAFUEL 03 harbors genes related to the biosynthesis of secondary metabolites, biofilm formation/regulation, and swarming motility that enhances its potential for controlling BLS in corn and suggests a promising candidate for the development of a commercial biocontrol agent.

Exploring the synergistic benefits of biochar and gibberellic acid in alleviating cadmium toxicity

Cadmium (Cd) toxicity significantly threatens agricultural productivity and food safety. Developing effective strategies to enhance plant tolerance to Cd stress is essential. This study investigates the synergistic effects of biochar (BC) and gibberellic acid (GA3) on mitigating Cd toxicity in maize (Zea mays), focusing on their impact on oxidative stress markers and antioxidant enzyme activities. Soil samples were collected from the Cholistan Institute of Desert Studies (CIDS) and analyzed for trace metal ions and other properties. Biochar was produced from fruit and vegetable waste, washed, washed, deashed, and mixed with 10 ppm GA3. FH-1036 hybrid maize seeds were sterilized and planted in pots containing soil with varying Cd levels (0, 8, and 16 mg Cd/kg soil). Twelve treatments were established, including control, GA3, BC, and their combinations under different Cd stress levels. Plants were irrigated to maintain 60% field capacity and harvested at the V10 growth stage. Hydrogen peroxide (H2O2) contents and activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) were measured in roots, stems, and leaves. Statistical analysis was performed using OriginPro 2021, with ANOVA and Fisher’s LSD test used to determine significant differences. GA3 and BC treatments significantly reduced H2O2 levels in maize roots, stems and leaves under Cd stress. The combined treatment of GA3 + BC showed the most significant reduction in H2O2 levels across all plant parts, reducing root H2O2 by 50%, stem H2O2 by 55%, and leaf H2O2 by 53% under severe Cd stress (16 mg Cd/kg). SOD activity increased under non-stress conditions but decreased under Cd stress, with the highest activity observed in the combined treatment. POD activity followed a similar pattern, with GA3 + BC treatment resulting in the most significant increases under non-stress conditions and the least reductions under Cd stress. CAT activity showed substantial increases with GA3 + BC treatment, particularly under severe Cd stress, with a notable rise over the control. APX activity also exhibited enhancements with GA3 and BC treatments, especially in the combined treatment under various Cd stress levels. This study highlights the potential of combined BC and GA3 treatments in improving Cd stress tolerance in maize. Future research should focus on field trials and the long-term impacts of these treatments on crop productivity and soil health.

Characterization of the Regulatory Network under Waterlogging Stress in Soybean Roots via Transcriptome Analysis.

Flooding stress caused by climate change is a serious threat to crop productivity. To enhance our understanding of flooding stress in soybean, we analyzed the transcriptome of the roots of soybean plants after waterlogging treatment for 10 days at the V2 growth stage. Through RNA sequencing analysis, 870 upregulated and 1129 downregulated differentially expressed genes (DEGs) were identified and characterized using Gene Ontology (GO) and MapMan software (version 3.6.0RC1). In the functional classification analysis, "alcohol biosynthetic process" was the most significantly enriched GO term in downregulated DEGs, and phytohormone-related genes such as ABA, cytokinin, and gibberellin were upregulated. Among the transcription factors (TFs) in DEGs, AP2/ERFs were the most abundant. Furthermore, our DEGs encompassed eight soybean orthologs from Arabidopsis and rice, such as 1-aminocyclopropane-1-carboxylate oxidase. Along with a co-functional network consisting of the TF and orthologs, the expression changes of those genes were tested in a waterlogging-resistant cultivar, PI567343. These findings contribute to the identification of candidate genes for waterlogging tolerance in soybean, which can enhance our understanding of waterlogging tolerance.

Performance of plant growth regulators in soybean cultivation under differente aplication methods

The utilization of biostimulants has emerged as a promising approach in agriculture, aiming to augment the physiological processes associated with germination and growth across diverse plant species, thereby leading to substantial and sustainable enhancements in agricultural productivity. Grounded in scientific insights, this study endeavored to juxtapose the efficacy of a biostimulant compound against conventional market-available products. The application methodologies encompassed seed treatment and foliar spraying at the V2 growth stage, with a primary focus on productivity augmentation. The investigation transpired at the Agricultural Technological Center of Paraguay (CETAPAR) in Colônia Iguazu, Paraguay, utilizing a randomized complete block design (RCBD) featuring 16 treatments replicated four times. Dosages were administered in adherence to label recommendations. Productivity data underwent rigorous statistical analyses, including the F-test and Dunnett's test, with a significance level set at 10%. Remarkably, foliar applications at the V2 phenological stage and seed treatments outperformed other modalities, yielding notable outputs of 4,431 kg.ha-1 and 4,419 kg.ha-1, respectively. Noteworthy is the observation that the tested biostimulant formulation exhibited equivalent or superior potential when juxtaposed with commercial counterparts. The findings underscore the viability of integrating biostimulants as a compelling strategy to optimize agricultural productivity, thereby furnishing farmers with an efficacious and sustainable alternative.

Machine learning and fluorosensing for estimation of maize nitrogen status at early growth-stages

Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 growth stages than at V6. Among the various machine learning models assessed, Support Vector Regression consistently outperformed the others across the three site-year datasets, delivering error estimates well within acceptable ranges and demonstrating the lowest RMSE values for maize nitrogen indicators.

Straw incorporation and nitrogen fertilization regulate soil quality, enzyme activities and maize crop productivity in dual maize cropping system

BackgroundStraw incorporation serves as an effective strategy to enhance soil fertility and soil microbial biomass carbon (SMBC), which in turn improves maize yield and agricultural sustainability. However, our understanding of nitrogen (N) fertilization and straw incorporation into soil microenvironment is still evolving. This study explored the impact of six N fertilization rates (N0, N100, N150, N200, N250, and N300) with and without straw incorporation on soil fertility, SMBC, enzyme activities, and maize yield.ResultsResults showed that both straw management and N fertilization significantly affected soil organic carbon (SOC), total N, SMBC, soil enzyme activities, and maize yield. Specifically, the N250 treatment combined with straw incorporation significantly increased SOC, total N, and SMBC compared to lower fertilization rates. Additionally, enzyme activities such as urease, cellulase, sucrose, catalase, and acid phosphatase reached their peak during the V6 growth stage in the N200 treatment under for both straw management conditions. Compared to N250 and N300 treatments of traditional planting, the N200 treatment with residue incorporation significantly increased yield by 8.30 and 4.22%, respectively. All measured parameters, except for cellulase activity, were significantly higher in spring than in the autumn across both study years, with notable increases observed in 2021.ConclusionsThese findings suggest that optimal levels of SOC, soil total N (STN), and SMBC, along with increased soil enzyme activities, is crucial for sustaining soil fertility and enhancing maize grain yield under straw incorporation and N200 treatments.

Application of Protein Hydrolysate Improved the Productivity of Soybean under Greenhouse Cultivation

Protein hydrolysates are plant biostimulants containing amino acids, oligopeptides, and peptides in their composition. When supplied to plants, protein hydrolysates (HPs) have been identified to improve nitrogen metabolism, enhance the activity of antioxidant enzymes, boost plant defense response to stresses, and positively impact the quantity and quality of products. Soybean is a crucial global commodity, with nitrogen being the primary nutrient for crop development as it directly affects productivity. This study aimed to evaluate the effect of an HP-based biostimulant on the N metabolism in nodulated soybean plants and their productivity. A greenhouse experiment was conducted to test two modes of application of the 0.20% HP-based biostimulant. Soybean plants, growing in pots, were treated with 0.20% HP either via seed treatment or foliar application (at growth stages V3 and V5). Activities of enzymes and compounds related to N metabolism, gene expression, and productivity components were analyzed. It was observed that the mode of application did not significantly influence the results. The application of HPs increased the concentration of nitrate, amino acids, and ureides in soybean leaves. It also positively altered the expression of genes such as nitrate reductase, urease, and asparagine. Additionally, it enhanced productivity, resulting in plants with a greater number and weight of pods and grains. Therefore, it is possible to consider HPs as a stimulator for increasing soybean productivity, even under non-stressing conditions.

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

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

Comparison and Optimal Method of Detecting the Number of Maize Seedlings Based on Deep Learning

Effective agricultural management in maize production operations starts with the early quantification of seedlings. Accurately determining plant presence allows growers to optimize planting density, allocate resources, and detect potential growth issues early on. This study provides a comprehensive analysis of the performance of various object detection models in maize production, with a focus on the effects of planting density, growth stages, and flight altitudes. The findings of this study demonstrate that one-stage models, particularly YOLOv8n and YOLOv5n, demonstrated superior performance with AP50 scores of 0.976 and 0.951, respectively, outperforming two-stage models in terms of resource efficiency and seedling quantification accuracy. YOLOv8n, along with Deformable DETR, Faster R-CNN, and YOLOv3-tiny, were identified for further examination based on their performance metrics and architectural features. The study also highlights the significant impact of plant density and growth stage on detection accuracy. Increased planting density and advanced growth stages (particularly V6) were associated with decreased model accuracy due to increased leaf overlap and image complexity. The V2–V3 growth stages were identified as the optimal periods for detection. Additionally, flight altitude negatively affected image resolution and detection accuracy, with higher altitudes leading to poorer performance. In field applications, YOLOv8n proved highly effective, maintaining robust performance across different agricultural settings and consistently achieving rRMSEs below 1.64% in high-yield fields. The model also demonstrated high reliability, with Recall, Precision, and F1 scores exceeding 99.00%, affirming its suitability for practical agricultural use. These findings suggest that UAV-based image collection systems employing models like YOLOv8n can significantly enhance the accuracy and efficiency of seedling detection in maize production. The research elucidates the critical factors that impact the accuracy of deep learning detection models in the context of corn seedling detection and selects a model suited for this specific task in practical agricultural production. These findings offer valuable insights into the application of object detection technology and lay a foundation for the future development of precision agriculture, particularly in optimizing deep learning models for varying environmental conditions that affect corn seedling detection.

Interseeded cover crops in wide‐row corn: An opportunity for northern cropping systems

AbstractCover crops are an effective way to reduce soil erosion and promote soil health. However, in North Dakota and other northern climates where corn (Zea mays L.) is an important commodity crop, killing frosts generally occur before harvest, leaving little opportunity for cover crop planting. By interseeding cover crops into corn during the growing season, the cover crops are given a longer period to establish. The purpose of this study was to identify the impact cover crops interseeded into wide‐row (60‐inch) corn have on soil water content and corn productivity. Two experimental sites were established in 2020 near Leonard and Rutland, ND. Both sites were organized into randomized complete block designs, with three cover crop treatments in Leonard (n = 9) and four cover crop treatments in Rutland (n = 16). Cover crops were no‐till drilled into the corn at the V4 growth stage. The cover crop treatments were diverse mixes developed to either provide pollinator habitat, overwinter, or winter‐kill. Throughout the growing season, soil gravimetric water content and cover crop biomass was monitored. At the end of the growing season, dry cover crop biomass ranged from 189 to 1445 lb ac−1. The presence and type of interseeded cover crops did not have a statistically significant effect on soil water content or corn yield. It is suspected the above average precipitation during the month of July led to adequate amounts of soil water for the entirety of the cover crop growing season, limiting the difference between treatments.

Registration of USDA‐N6006 soybean germplasm combining high yield, flood tolerance, and elevated oil content

AbstractUSDA‐N6006 soybean [Glycine max (L.) Merr.] (Reg. no. GP‐527, PI 704140) is a conventional F4–derived early maturity group (MG) VI germplasm jointly released by USDA‐ARS and North Carolina Agricultural Research Service in 2023. USDA‐N6006 is the first MG VI, publicly released germplasm that combines flood tolerance with high‐yield lodging resistance, and elevated seed oil. Over 19 environments of the MG VI USDA Southern Uniform Soybean Trials (2017–2018), seed yield of USDA‐N6006 (3393 kg ha−1) was similar to high‐yielding lodging‐resistant check cultivars ‘NC‐Dunphy’ (3427 kg ha−1) and ‘NC‐Dilday’ (3475 kg ha−1). It matured 1 day earlier than NC‐Dunphy and exhibited similar lodging scores. Over 5 years of testing in North Carolina (2016–2019), USDA‐N6006 exhibited very robust flood tolerance. Mean flooding scores were numerically or statistically (p < 0.05) superior to the four flood‐tolerant controls at both V4 and R1 growth stages. USDA‐N6006 was not intentionally bred to be flood tolerant and identified as such only through an extensive screening of “in house” USDA breeding lines. USDA‐N6006 and most of its antecedents are unique in that they are derived from unreleased breeding lines developed at the flood prone Tidewater Research station in North Carolina. Inadvertent selection for flood tolerance may have been a part of the USDA's long‐term breeding effort to improve yield. Seed oil content of USDA‐N6006 (237 g kg−1) was similar to that of elevated seed oil cultivar NC‐Dilday, but significantly higher than NC‐Dunphy (228 g kg−1) in the Uniform Trials. Seed protein content and estimated meal protein contents of USDA‐N6006 (389 g kg−1 and 46.4%, respectively) were similar to that of NC‐Dunphy. USDA‐N6006 should be excellent parental stock for applied breeding in the southern United States.

Ecological and economic analysis of insecticidal control of fall armyworm

Fall Armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae) , is a significant pest causing substantial economic losses worldwide, particularly in Sub-Saharan Africa. The prevalent strategy for managing FAW involves insecticide applications, ranging from synthetic to botanical and microbial agents. However, the ecological and economic impacts of these interventions often remain unassessed. This study scrutinizes the ecological and economic viability of two insecticidal treatments: one based on emamectin benzoate and another comprising a combination of Pieris rapae granulovirus (PrGV) and Bacillus thuringiensis subsp. kurstaki (Btk) with varied application timings and frequencies. Our findings indicate that both insecticide types were effective in reducing FAW larval populations and the associated crop damage. From an ecological standpoint, the PrGV|Btk treatment enhanced parasitism rates, especially when applied only at the early stage of the crop. However, this approach did not significantly lower crop damage compared to a "targeted" strategy, where insecticide application was contingent upon observed injury levels. Remarkably, the "targeted" strategy led to increased FAW larval parasitism, particularly at the V7 growth stage of the crop. Economically, the "targeted" insecticide application emerged as both effective and efficient, minimizing the need for multiple sprays and thus recommended for managing FAW infestations while considering cost and ecological balance.

Peanut (Arachis hypogaea L.) response to low‐rate applications of selected herbicides at vegetative and reproductive growth stages

AbstractOff‐target drift of herbicides can seriously reduce peanut (Arachis hypogea L.) growth and yield and is of great concern to growers who will need to manage sensitive crops near new herbicide‐tolerant crops. Field experiments were conducted in 2021 and 2022 with 25% labeled rates of dicamba, glufosinate, glyphosate, lactofen, and paraquat to simulate drift on peanut. The objective was to evaluate the effects of low‐rate application of the herbicides on peanut injury and yield reductions and to determine if unmanned aerial vehicle (UAV) imagery‐based normalized difference vegetation index (NDVI) provides accurate estimation of peanut injury from the herbicides applied at vegetative (V3) and reproductive (R3) growth stages. Peanut suffered greater yield reduction (33%) when exposed to the herbicides at R3 than at V3 growth stage (19%) across all herbicides applied. The order of herbicides that induced yield reductions in peanut was glyphosate > glufosinate = dicamba > paraquat = lactofen. Regardless of exposure timing, NDVI values generated from UAV imagery could not differentiate paraquat or lactofen injury from the weed‐free check. However, NDVI values could differentiate between injured and weed‐free check plants up to 2 and 4 weeks after treatment (WAT) for dicamba at R3 and V3 exposure timing, respectively, up to 4 WAT for glufosinate, and 8 WAT for glyphosate. NDVI from aerial imagery may be helpful to accelerate the detection of injury in large hectarages with greater accuracy compared with visual injury rating, which can be influenced by individual estimation bias.

Toward improved image‐based root phenotyping: Handling temporal and cross‐site domain shifts in crop root segmentation models

AbstractCrop root segmentation models developed through deep learning have increased the throughput of in situ crop phenotyping studies. However, models trained to identify roots in one image dataset may not accurately identify roots in another dataset, especially when the new dataset contains known differences, called domain shifts. The objective of this study was to quantify how model performance changes when models are used to segment image datasets that contain domain shifts and evaluate approaches to reduce error associated with domain shifts. We collected maize root images at two growth stages (V7 and R2) in a field experiment and manually segmented images to measure total root length (TRL). We developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift. For the V7 growth stage, a growth‐stage‐specific model trained only on images captured at the V7 growth stage was best suited for measuring TRL. At the R2 growth stage, combining images from both growth stages into a single dataset to train a model resulted in the most accurate TRL measurements. We applied two of the field models to images from a greenhouse experiment to evaluate how model performance changed when exposed to a cross‐site domain shift. Field models were less accurate than models trained only on the greenhouse images even when crop growth stage was identical. Although models may perform well for one experiment, model error increases when applied to images from different experiments even when crop species, growth stage, and soil type are similar.

Urea ammonium nitrate placement methods, row patterns, and irrigation effects on corn productivity in a humid subtropical region

AbstractCrop yields are adversely affected by nitrogen (N) losses in humid subtropical regions. This study was conducted to determine whether N dynamics could be manipulated through planting geometry and fertilizer placement. The effects of irrigation (irrigated and rainfed), row pattern (single‐ and twin‐row), and N placement (surface dribble, one knife, two knives, and control) on corn (Zea mays L.) productivity and N use efficiency were investigated at Leland, Mississippi, on a Bosket very fine sandy loam. The total N rate was split into two equal halves of 128 kg N ha−1 at V2 and V6 growth stages. The effects of row pattern and N placement were consistent between irrigated and rainfed environments. There was no interaction between row pattern and N placement nor did row pattern affect corn productivity or N use efficiency parameters. In 2020, when less than 30 mm of rainfall occurred from the first N application through 2 weeks after the last N application, corn grain yield and agronomic N efficiency were not different among placement methods and averaged 11.1 Mg ha−1 and 20.3 kg grain kg−1 fertilizer N, respectively. However, in 2021, applying N with one knife increased corn grain yield by 7% to 14% compared to the two knives and surface dribble application methods; in that year, 235 mm of rainfall occurred from the first N application through 2 weeks after the last N application. Applying N with one knife appears to consistently improve crop productivity and N use efficiency parameters for both irrigated and rainfed environments.

Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies

Highlights The response of dicamba-susceptible soybean to three simulated rates and growth stages has been studied. Soybean physiological data, such as plant height and phototoxicity, was collected at the selected growth stage. Aerial images of the experiment field using UAV were collected on specific days after treatment. Soybean yield, biological response, and the correlation of vegetative indices in predicting soybean yield were achieved. Abstract. Many dicotyledonous weeds can be selectively controlled by dicamba application during the growing season. However, vapor or spray drift from dicamba application could cause significant yield loss on non-target species such as soybean [Glycine max (L.) Merrill] that do not contain the dicamba resistance trait. In this study, the impact of simulated dicamba drift was investigated on dicamba-susceptible soybeans during different growth stages. A field experiment was conducted employing small unmanned aircraft systems (sUAS) and multispectral image sensors to assess the dose-response relationship between dicamba drift and soybean yield. The experiment followed a randomized complete block design, with main plots representing three distinct soybean growth stages (third trifoliate, sixth trifoliate, and early reproductive stage) and sub-plots encompassing various simulated dicamba drift rates. The analysis, involving non-linear regression and normalized difference vegetation index (NDVI) regression, revealed that visible soybean injuries were observed at minimal dicamba rates. However, significant yield loss occurred during the V6 growth stage at a dicamba rate of 98 g.ai ha-1, emphasizing the critical growth stage sensitivity. Moreover, a moderate yet meaningful relationship (R2 = 0.46) was established between sUAS multispectral NDVI data and soybean yield, highlighting the potential for sUAS with multispectral sensors to predict soybean yield losses. These results contribute valuable insights into the impact of dicamba drift on soybeans and the efficacy of remote sensing technology in assessing yield outcomes. Keywords: Dicamba-drift injury, Multispectral image, Precision agriculture, Soybean yield, sUAS, Yield prediction.

Light partitioning strategies impact relative fitness of weeds and cover crops when drill-interseeding in corn

AbstractDrill-interseeding cover crops into corn (Zea mays L.) is an emerging establishment method in northern U.S. production regions. However, cover crop performance in interseeded systems remains variable, and creating environments that are conducive to cover crop but not weed growth is challenging. Cultural practices that partition resources between corn and interseeded cover crops have potential to improve performance if weeds are adequately managed. This study evaluated interactions among corn hybrids differing in leaf architecture (upright, pendulum), corn row spacing (76 cm, 152 cm), and interseeding timing (V3, V6) on light transmittance, relative fitness of cover crop species (cereal rye [Secale cereale L.], annual ryegrass (Lolium multiflorum Lam), red clover [Trifolium pratense L.]) and weeds, and corn grain yield at three U.S. Northeast locations. Results showed that light transmittance through the corn canopy was greater in 152-cm row spacing compared with 76-cm row spacing at the V6 growth stage, with the magnitude of difference increasing at the V10 corn growth stage. Corn hybrids had a marginal effect on light transmittance. The effect of row spacing and interseeding timing on fall cover crop biomass varied across cover crop species and locations. In 76-cm rows, interseeding earlier (V3) increased cover crop biomass production. The relative fitness of cover crops was greater than that of weeds in each combination of cultural practices that included narrow spacing (76 cm), whereas the relative fitness of weeds was greater than that of cover crops when interseeding in wide rows (152 cm). The effect of row spacing on corn yield varied among locations, with higher yields observed in 76-cm row spacing compared with 152-cm at two of three locations. Our results show that interseeding early (V3) on 76-cm row spacing can balance cover crop and corn production management goals, while placing cover crops at a relative fitness advantage over weeds.

Exploring the Root Morphological Traits of Diverse-Origin Cultivated Soybean

Root morphological traits (RMTs) profoundly influence plant growth, resilience to abiotic stresses, and yield in soybean (Glycine max). In a comprehensive study spanning two consecutive years (2021–2022), the RMTs were assessed in 216 soybean accessions from 34 diverse origins. The investigation involved randomized batches with plants cultivated in PVC pipes filled with horticultural soil and harvested at the V2 growth stage. All the germplasms exhibited significant differences (p < 0.001) in all measured traits, i.e., total root length (TRL), root volume (RV), average diameter (AD), number of tips (NT), number of forks (NF), and tertiary total length (TTL). Among the top 5% performers in TRL, which, interestingly, were exclusively of Korean origin, germplasm IT115491 displayed an impressive average TRL value of 1426.24 cm. Notably, germplasms from Serbia and Korea predominantly occupied the upper AD quantile, with IT156262 exhibiting the highest AD value of 0.57 mm. A correlation analysis showed strong positive associations of TRL with RV (r = 0.85), NT (r = 0.84), NF (r = 0.96), and TTL (r = 0.88), whereas it had a negative association with AD (r = −0.25). A principal component analysis (PCA) showed a cumulative 95% of the total variance in the data in the first three principal components (PCs). PC1 (eigenvalue = 4.64) accounted for a 77.00% variance, with TRL, RV, NF, NT, and TTL exhibiting the highest associated eigenvectors. K-means clustering was performed with three clusters. Cluster 2 contained accessions with higher AD values, whereas Cluster 3 comprised accessions with increased TRL, NT, NF, and TTL, which mostly originated from Korea. Our findings offer targeted insights for plant breeders to optimize specific root traits and enhance crop performance across diverse environmental conditions by strategically targeting these clusters. Additionally, the influence of cultivar origin on root traits warrants further investigation, with implications for future breeding programs.