Soybean Leaf Area Index Research Articles

Response of Evapotranspiration, Photosynthetic Characteristics and Yield of Soybeans to Groundwater Depth

To clarify the physiological mechanism of different groundwater depths affecting soybean evapotranspiration, photosynthetic characteristics and yield, a field experiment with four groundwater depth levels (1 m (D1), 2 m (D2), 3 m (D3) and 4 m (D4)) was conducted through the groundwater simulation system in 2021 and 2022. In this study, a quantitative analysis was conducted on the groundwater recharge and irrigation water demand and evapotranspiration (ET) of soybean fields with different treatments, and the effects of different treatments on soybean leaf area index (LAI), chlorophyll content index (SPAD), intercepted photosynthetic active radiation (IPAR), photosynthetic gas exchange parameters, dry matter accumulation (DMA) and yield were explored. The results showed the following: (1) Groundwater depth affected soybean ET and the source of ET. With the increase in groundwater depth, groundwater recharge and its contribution to ET gradually decreased, but the amount of irrigation required gradually increased, resulting in the ET as D1 > D4 > D2 > D3. (2) Soybean LAI, SPAD and IPAR were significantly affected by the different groundwater depths, of which the D1 treatment always maintained the maximum, followed by the D4 treatment, and the D3 treatment was the minimum. The photosynthetic gas exchange parameters under different treatments changed synergistically, showing significant differences in the flowering and podding stages, notably D1 > D4 > D2 > D3. Soybean DMA and yield first decreased and then increased with the increase in groundwater depth, and the average DMA and yield under the D1 treatment increased by 27.71%, 46.80% and 22.82% and 20.29%, 29.91% and 12.83% in the two years, respectively, compared to the D2, D3 and D4 treatments. (3) The structural equation model demonstrated that the groundwater depth indirectly affected the growth of soybean leaf area by affecting groundwater recharge, which in turn regulated soybean ET and photosynthetic capacity and ultimately affected DMA and yield. The above results showed that in the case of shallow groundwater depth (D1), the largest groundwater recharge promoted the growth of soybean leaf area and chlorophyll synthesis and increased the absorption and utilization of solar radiation. And it improved the leaf stomata conditions, accelerated the gas exchange between the plant and atmosphere, enhanced the photosynthetic production capacity and ET and achieved maximum DMA and yield. Soybean leaf growth and photosynthesis diminish with the increase in groundwater depth. In the case of deep groundwater depth (D4), the maximum irrigation improved the growth and photosynthetic performance of soybean leaves, which was favorable to ET, and ultimately led to increases in DMA and yield.

Dynamic simulation of leaf area index for the soybean canopy based on 3D reconstruction

Leaf area index (LAI) of the soybean canopy was an important indicator to reflect the growth and development of the soybean plant. However, the traditional LAI measurement in the field was expensive, time-consuming, and challenging to achieve high accuracy. Thus, in this study, a calculation method of LAI for the soybean canopy based on 3D reconstruction was proposed, and the dynamic simulation model of canopy LAI was established. First, northeast soybean varieties of Kangxianchong8 and Dongnong252 were taken as the research objects, and a multi-source image synchronous acquisition platform for soybean canopy based on Kinect 2.0 was constructed to obtain the canopy image data from the V3 to R7 growth periods. Second, the 3D structure of the soybean canopy was reconstructed by conditional filtering and statistical filtering. Third, a soybean LAI estimation method was established by canopy analysis. The determination coefficient R2 between the calculated value and the standard value of LAI was greater than 0.99. Finally, a dynamic simulation model of soybean LAI was established based on the Richards model and the genetic parameters of varieties. The results showed that the accuracy of the dynamic simulation model reached above 0.99, which realized the critical technology of rapid detection and dynamic simulation of LAI for the soybean canopy, and provided quantitative dynamic prediction and technical support for scientific regulation of ecology and morphology for the soybean canopy.

Manejo da ferrugem asiática da soja baseado no índice de área foliar e fungicida multissítio

ABSTRACT Soybean foliar diseases negatively affect leaf area index (LAI) and compromise the main structure for photosynthesis and grain yield. The objective of this study was to propose a new methodology to determine the commencement of fungicide application based on the soybean LAI, spray volumes, and the inclusion of a multisite fungicide to control Asian soybean rust (ASR). The study was implemented in a randomized block design at two locations, with four replicates in a factorial scheme plus one additional treatment (4 × 2 × 2 + 1) - four soybean LAI (0.9, 1.8, 3.6, and 6.1) indicating the initiation stages of fungicide applications, two spray volumes (70 and 120 L ha-1), with or without multisite fungicide (mancozeb), and a control treatment without fungicide. The spray deposition on plants did not differ between the treatments for 1.8 and 3.6 LAI. Treatments including the multisite fungicide for LAI between 3.6 and 6.1 for both spray volumes presented superior ASR control results. The highest soybean yields were observed when fungicide application started at 3.6 LAI for both spray volumes. The best correlation between disease control and grain yield occurred when the applications started at 3.6 LAI with 70 L ha-1 spray volume and the multisite fungicide.

Fungicide Spraying Programs Reducing Asian Soybean Rust Impact on Soybean Yield Components

Soybean is one of the leading agricultural commodities, and Brazil is currently the largest producer globally. Despite this, fungal diseases as Asian Soybean Rust (ASR) are among the primary limiters of high crop yields in Brazilian fields. Losses caused by the biotrophic fungus Phakopsora pachyrhizi can reach up to 90%, depending on weather conditions, and several components can be affected during soybean growth and reproduction. Here, we assessed fungicide spraying programs to ASR control, aiming to reduce the losses on soybean components. The experiments were performed under field conditions during the harvest season 2014/2015. The evaluated variables were soybean leaf area index, ASR severity, yield components, dry mass grain accumulation, protein, and oil content. The yield components assessed were the number of pods per plant, seeds per pod, seeds per plant, and thousand-grain weight. The disease severity gradient was generated using seven fungicide spraying programs, differing in time, number, and type of fungicides. Three fungicide programs that included applications in the soybean vegetative and reproductive stages were more efficient. These programs resulted in the lower area under the disease progress curve (AUDPC), greater leaf area duration (LAD), and health leaf area duration (HAD) than the untreated soybean. The ASR infection in soybean resulted in reduced LAD and HAD, and as a consequence, interfered negatively with dry matter accumulation, yield components, and grain yield.

Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data

Crop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, and agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and spectral vegetation indices extracted from Sentinel-2 optical data to estimate crop LAI and wet and dry biomass. Various machine learning algorithms, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were trained and assessed for three major crops (wheat, soybeans and canola). ANN provided the best accuracy for all wheat parameters and soybean LAI and canola wet biomass and LAI. RFR led to higher accuracy for soybean dry and wet biomass. However, SVR could accurately estimate only canola dry biomass. All data were then pooled to investigate if a single algorithm could estimate biophysical parameters for all crops. The RFR model accurately estimated wet and dry biomass and LAI across all crop types in this scenario. This generic model is fast and accurate and can be easily applied for crop mapping and monitoring over large geographies using cloud computing platforms, such as Google Earth Engine.

Are economic thresholds for IPM decisions the same for low LAI soybean cultivars in Brazil?

Economic thresholds (ETs) are well-established for defoliation of soybean, Glycine max, and have been updated for many of the newer cultivars; however, there is increasing grower adoption of cultivars with a reduced leaf area index (LAI). It is of theoretical and practical interest to determine low LAI cultivar tolerance to defoliation. We conducted experiments during two consecutive crop seasons (2017/2018 and 2018/2019) using three soybean cultivars (NS 5959 IPRO, NS 5445 IPRO, and DON MARIO 5.8i) and three defoliation levels (0%, 16.7%, and 33.3%) to evaluate the tolerance of reduced LAI soybean cultivars under different defoliation levels. We observed differences among cultivar's LAI during plant development during both years. Soybean LAI was reduced with increasing defoliation intensity. Tested continuous defoliation levels from plant development stages of V2 to R6 reduced the weight of 1000 seeds and yield but did not impact oil or protein content. Despite our findings that current ET for defoliators in soybean (30% defoliation during vegetative stage and 15% defoliation during reproductive stage) are valid, it is important to consider that continuous defoliation injury impacts the capacity of the plant to respond to injury and must be further evaluated for ET refinement in future research. © 2020 Society of Chemical Industry.

Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.

Soybean and common ragweed (Ambrosia artemisiifolia) growth in monoculture and mixture

AbstractUnderstanding how plants alter their growth in response to interplant competition is an overlooked but complex problem. Previous studies have characterized the effect of light and water stress on soybean or common ragweed growth in monoculture, but no study has characterized soybean and common ragweed growth in mixture. A field study was conducted in 2015 and 2016 at the University of Nebraska-Lincoln to characterize the growth response of soybean and common ragweed with different irrigation levels and intraspecific and interspecific interference. The experiment was arranged in a split-plot design with irrigation level (0, 50%, 100% replacement of simulated evapotranspiration) as the main plot and common ragweed density (0, 2, 6, 12 plants m−1 row) as the subplot. Crop- and weed-free controls and three mixture treatments were included as subplots. Periodic destructive samples of leaf area and biomass of different organ groups were collected, and leaf area index (LAI), aboveground biomass partitioning, specific leaf area (SLA), and leaf area ratio (LAR) were calculated. Additionally, soybean and common ragweed yield were harvested, and 100-seed weight and seed production were determined. Soybean did not alter biomass partitioning, SLA, or LAR in mixture with common ragweed. Soybean LAI, biomass, and seed size were affected by increasing common ragweed density. Conversely, common ragweed partitioned less new biomass to leaves and increased SLA in response to increased interference. Common ragweed LAI, biomass, and seed number were reduced by the presence of soybean and increasing common ragweed density; however, seed weight was not affected. Results show that adjustment in biomass partitioning, SLA, and LAR is not the method that soybean uses to remain plastic under competition for light. Common ragweed demonstrated plasticity in both biomass partitioning and SLA, indicating an ability to maintain productivity under intra- and inter-specific competition for light or soil resources.

Optimum Leaf Area Index to Reach Soybean Yield Potential in Subtropical Environment

Core Ideas The optimum leaf area index (LAI) at beginning bloom to reach the yield potential is between 3.6 (indeterminate stem termination) and 4.5 (determinate stem termination).The optimum LAI maximum to reach the yield potential is between 6.0 and 6.5 for indeterminate and determined cultivars.The optimum LAI for high yield potential should be adjusted according to the maturity group, stem termination and sowing date.The increase in yield was observed with the increase of LAI for experiments and farms with a high technological level (soybean yield more than 4.5 Mg ha−1). The interactive influence of climate and management factors on leaf area index (LAI) of soybean yield potential has not been investigated in subtropical environments. Such information can help in optimizing current soybean management practices to increase seed yield and efficiency of resource use. The objective of this study was to identify the optimum leaf area index for attaining yield potential in modern soybean cultivars grown in a subtropical environment. A large soybean yield, LAI, and phenology database collected from a combination of on‐farm and research‐station experiments conducted during four soybean growing seasons (2011–2015) in southern Brazil was used. The database represented a wide range of weather conditions, soil types, sowing dates, and cultivar maturity groups (MGs). A boundary function was derived for the relationship between soybean yield and LAI at R1 and R5 developmental stages for cultivars of determinate and indeterminate growth habit, and for both types combined. An increase in yield was in line with the increase of LAI for experiments with a small yield gap (soybean yield more than 4.5 Mg ha−1). In this case, optimum LAI at R1 growth stage (LAIR1, opt) to reach the soybean yield potential was between 3.4 (indeterminate) and 4.5 (determinate) whereas the optimal maximum LAI (LAImax,opt) is between 6.0 and 6.5 for indeterminate and determinate cultivars, respectively. These results suggested that management practices for soybean crops that involve the LAI and high yield potential must be adjusted according to the maturity group, growth habit and sowing date in a subtropical environment.

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).

Intercomparison of ADC-Lite Images on UAV and TM Simulation Data for Soybean Leaf Area Index Retrieval

Intercomparison of ADC-Lite Images on UAV and TM Simulation Data for Soybean Leaf Area Index Retrieval

Algorithms for estimating green leaf area index in C3 and C4 crops for MODIS, Landsat TM/ETM+, MERIS, Sentinel MSI/OLCI, and Venµs sensors

This study developed a set of algorithms for satellite mapping of green leaf area index (LAI) in C3 and C4 crops. In situ hyperspectral reflectance and green LAI data, collected across eight years (2001–2008) at three AmeriFlux sites in Nebraska USA over irrigated and rain-fed maize and soybean, were used for algorithm development. The hyperspectral reflectance was resampled to simulate the spectral bands of sensors aboard operational satellites (Aqua and Terra: MODIS, Landsat: TM/ETM+), a legacy satellite (Envisat: MERIS), and future satellites (Sentinel-2, Sentinel-3, and Venµs). Among 15 vegetation indices (VIs) examined, five VIs – wide dynamic range vegetation index (WDRVI), green WDRVI, red edge WDRVI, and green and red edge chlorophyll indices – had a minimal noise equivalent for estimating maize and soybean green LAI ranging from 0 to 6.5 m2 m−2. The algorithms were validated using MODIS, TM/ETM+, and MERIS satellite data. The root mean square error of green LAI prediction in both crops from all sensors examined in this study ranged from 0.73 to 0.95 m2 m−2 and coefficient of variation ranged between 17.0 and 29.3%. The algorithms using the red edge bands of MERIS and future space systems Sentinel-2, Sentinel-3, and Venµs allowed accurate green LAI estimation over areas containing maize and soybean with no re-parameterization.

INFLUENCE OF PHOSPHORUS APPLICATION WITH AVAIL ON FOLLOWING SOYBEANS IN SOUTHEASTERN COASTAL PLAINS

Application of Phosphorus (P) in combination of with polymer Avail to previous crop may affect soybeans [Glycine max (L.) Merr.] under dryland conditions. The objective of this study was to determine the effect of two P rates (45 and 90 kg P2O5 ha-1) with and without Avail applied to winter wheat (Triticum aestivum L.) previous crop on growth and yields of dryland soybeans near Blackville, SC from 2011 to 2012. Soybeans were evaluated for Leaf Area Index (LAI), plant Normalized Difference Vegetation Index (NDVI), plant height, grain moisture, seed weight and grain yields. Compared to untreated control, P applications at 45 kg and 90 kg ha-1 with Avail to winter wheat significantly increased soybean grain yields by 12.3% and 20.2%, respectively. Phosphorus applied to previous crop at 45 kg and 90 kg P ha-1 with polymer Avail, improved soybean yields by 8.1 and 4.0% over P treatments not treated with Avail, respectively. Soybean LAI significantly increased by 22.8% at 3 months after planting with application of P at 90 kg ha-1 and polymer Avail to winter wheat previous crop compared to untreated control. Lower P rate of 45 kg ha-1 with Avail to winter wheat increased soybean LAI by 11.8%. Applications of 45 kg and 90 kg P ha-1 with Avail to previous crop improved plant NDVI at 3 months after soybean planting by 1.8% and 2.5%, respectively. Based on significant linear relationship, increasing plant NDVI by 0.1 improved soybean yields by 126.1 kg ha-1. Applications of P with Avail to winter wheat did not affect soybean LAI and NDVI at 2 months after planting, plant height, grain moisture and seed weight. Results from this study indicate that P applications with Avail to previous crop significantly improved plant LAI and NDVI at 3 months after planting and increased soybean yields.

The sensitivity of RADARSAT-2 polarimetric SAR data to corn and soybean leaf area index

In this study, quadrature-polarization (quad-pol) RADARSAT-2 data at steep (25o) and shallow (40o) incidence angles were acquired during the 2008 season, imaging 13 corn and soybean fields. The leaf area index (LAI) was derived from optical imagery, and volumetric soil moisture was measured coincident with each overpass. Many synthetic aperture radar (SAR) parameters were significantly correlated with derived corn and soybean LAI. The highest correlations were observed for parameters sensitive to volume scattering (HV, LL, and RR backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter) at the steeper angle. For corn, the minimum correlation coefficient was 0.95. For soybeans, the coefficients were between 0.83 and 0.86. Sensitivity to LAI was lost late in the season, when the derived LAI exceeded 3.0 m2m−2. The derived LAI and the measured soil moisture were used to model several radar parameters (HV backscatter, pedestal height, and the Freeman–Durden volume-scattering parameter) using the water-cloud model. Early in the season, the SAR response was primarily affected by the vegetation, but soil moisture was also an important contributor. When the derived LAI exceeded 1, soil-moisture contributions became minimal. The water-cloud model adequately simulated SAR responses as the canopy developed and LAI increased, demonstrating the potential of polarimetric SAR data for monitoring indicators of crop productivity.

Influence of Seeding Rate on Weed Density in Soybean Planting System for Southeastern Coastal Plains

Problem statement: Increasing seeding rates may help decrease weed pressure in soybean [Glycine max (L.) Merr.] wide row spacing. Approach: The objective of this study was to evaluate the influence of five glyphosate-resistant soybean Maturity Groups (MG) (IV, V, VI, VII and VIII) and six seeding rates (68,000,136,000, 204,000, 272,000, 340,000 and 408,000 seeds ha-1) on weed density under dryland conditions on the Southeastern coastal plain in 2007-2009. Results: Weed decrease with increasing seeding rate varied over years. Weed density was generally lower at higher seeding rates for most MG soybeans at 30 and 60 DAP, except MG IV and VIII at 30 DAP in 2007 and MG VI at 30 DAP in 2008. At 60 DAP, soybean leaf area index (LAI) and normalized difference vegetation index (NDVI) were greater with lower weed density. Conclusion: Additionally, negative correlations were observed between weed density and plant LAI/NDVI for all MG in 2008 and MG IV through VI in 2009. These results suggest that increased seeding rates may help decrease weed pressure and improve soybean growth at early growth stages. However the response of weed pressure to seeding rate may vary over years and depend on MG soybean.

Effect of Soybean Row Width and Population on Weeds, Crop Yield, and Economic Return

Field studies were conducted in 2004 and 2005 to determine the effect of soybean row width and population on weeds, canopy closure, crop yield, and economic return in glyphosate-resistant soybean. Soybean leaf area index (LAI) was greater in 19- and 38-cm, compared with 76-cm rows from 8 to 12 wk after planting in the low, moderate, and high soybean populations. Canopy closure was delayed by 2 wk in the moderate population in 76-cm rows compared with the high population in 19-cm rows. Fewer weeds emerged in 19-cm, compared with 76-cm rows following glyphosate application, and increasing the soybean population within a row width did not influence late-season weed emergence. Weed biomass in the weedy control was greater in the very low soybean population compared with the high soybean population within each row width; however, weed biomass in the weedy control was similar in the high and moderate soybean populations. Soybean yield in the weed-free and 10-cm glyphosate treatment did not differ, and yield was greater in 19-cm rows planted at moderate or high, compared with low populations. There was no difference in weed-free soybean yield at low, moderate, and high populations within 38- and 76-cm rows. Gross margins were usually greater in 19- and 38-cm, compared with 76-cm rows. The gross margin for soybean planted in 19-cm rows was usually greater at moderate or high soybean populations compared with lower populations. In 76-cm rows, the gross margin was greatest at the low and moderate soybean populations. When rainfall or other factors limited soybean yield, increasing the soybean population from approximately 300,000 plants/ha to 445,000 plants/ha in 19-, 38-, and 76-cm rows did not result in quicker canopy closure, reduced weed emergence, or greater soybean yield and gross margins.Nomenclature: Glyphosate potassium salt, glyphosate-resistant soybean, Glycine max (L.) Merr., ‘AG2701’

Residue Management Practice Effects on Soybean Establishment and Growth in a Young Wheat-Soybean Double-Cropping System

ABSTRACT Double-cropping wheat (Triticum aestivum L.) and soybean [Glycine max (L.) Merr.] is popular throughout the mid-southern United States. To ensure an adequate stand, it is imperative that soybean be planted as soon as possible after wheat harvest due to an already shortened growing season following wheat. Typically, wheat residue is burned, and then conventional tillage (CT) is used to prepare a seedbed for soybean planting. However, residue burning has serious negative environmental consequences. The objective of this study was to examine effects of tillage [CT and no-tillage (NT)], residue burning (burn and no burn), and wheat-residue level (high and low) on soybean stand establishment, growth, and production over three cropping cycles at two locations on silt-loam Alfisols in eastern Arkansas. Soybean plant populations between 10 and 30 days after planting were higher (p < 0.02) under NT than CT in most year-location combinations, while most without wheat residue burning were equivalent to those with burning. Soybean plant populations were unaffected by N rate/wheat-residue level in most year-location combinations. Mid-season soybean leaf area index was higher (p < 0.03) under NT than CT for two of four year-location combinations. Soybean yields under NT were equivalent to yields under CT in all year-location combinations. Results indicate that the alternative pre-plant field preparation combination of no residue burning followed by NT can perform as well as the more traditional combination of burning followed by CT in the wheat-soybean double-crop system in the mid-southern United States.

Simulated insect defoliation and duration of weed interference affected soybean growth

An improved understanding of crop stress from multiple pests is needed for better implementation of integrated pest management (IPM) strategies. Field studies were conducted in 2003 and 2004 at two locations in eastern Nebraska to describe the effects of simulated early-season insect defoliation of soybean and duration of weed interference on soybean growth. Three levels of simulated defoliation (undefoliated, 30, and 60%) and seven durations of weed interference (weedy and weed free; weed removal at V2, V4, V6, R3, and R5) were evaluated in a split-plot design. Defoliation significantly reduced soybean leaf-area index (LAI), total dry matter (TDM), and crop height in season-long weedy treatments only. Biomass partitioning during vegetative and reproductive growth was affected by both defoliation and weed interference. Increase in soybean relative growth rate (RGR) and biomass production soon after defoliation occurred (e.g., V5 stage) indicated potential defense mechanism by which soybean is able to adjust its physiology in response to the loss of leaf area. Weed interference combined with defoliation caused the greatest yield losses up to 97%. Results from this study indicate the need for monitoring early-season insect density and weed growth to determine if simultaneous control of both pests may be needed.

Absence of Interactive Responses of Early Soybean (Glycine max) Growth to Soybean Cyst Nematode (Heterodera glycines), Postemergence Herbicides, and Soil pH and Texture1

Farmer observations and previous studies indicated that reductions in soybean yield caused by the soybean cyst nematode (SCN) are greater when other stresses, biotic or abiotic, are present. Also, it has been reported that the effect of SCN on soybean growth depended on factors such as soil pH, soil texture, and herbicides. Although postemergence herbicides may adversely affect soybean metabolism, acifluorfen can reduce SCN infection. The objective of the present study was to determine the main and interactive effects of SCN egg population density (SCND), soil pH, soil texture, and the application of the herbicides acifluorfen, glyphosate, and imazethapyr on early soybean growth. Greenhouse studies assessed different combinations of these factors for 65 d after planting. No interactions were observed for any of the main effects. Soil pH and texture did not affect soybean growth. SCND was the only main effect that explained soybean growth reductions. The effect of SCND on soybean growth was exhibited as 15–50% decreases of leaf area index (LAI) and dry weight in all cases, but reductions in plant height also were observed. No relationship between SCND and the number of SCN eggs recovered at the end of the experiment was observed. Herbicides did not reduce soybean growth, although acifluorfen consistently caused the highest soybean injury reaching 18–20% from 1–14 days after application (DAA). At 50 DAA, acifluorfen injury was negligible, and soybean LAI and dry weight did not differ from the nontreated control. These results indicated that the effect of SCN on soybean growth was not directly affected by the other evaluated main effects. Therefore, trends observed in the field that suggested interactions between those factors are likely the result of other factors not considered in the present study or to more complex relationships between factors analyzed in the present study and other elements present in the field.Nomenclature: Acifluorfen, glyphosate, imazethapyr; soybean; soybean cyst nematode, Heterodera glycines Ichinoche.Additional index words: Herbicide injury, nematode infection.Abbreviations: AMS, ammonium sulfate; COC, crop oil concentrate; DAA, days after application; DAP, days after planting; LAI, leaf area index: MSO, methylated seed oil; PPFD, photosynthetic photon flux density; SCN, soybean cyst nematode; SCND, SCN egg population density.

A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.