Soybean Leaf Area Research Articles

Soybean yield was not influenced by foliar insecticide application at R3 and R5 stages

AbstractDue to low cost, farmers often combine foliar insecticide with a foliar fungicide application without assessing insect activity in their soybean [Glycine max (L.) Merr.] field. Therefore, this research was conducted to determine if prophylactic application of foliar insecticide improves soybean yield in Ohio. Objectives were to evaluate the effect of foliar insecticide applied at the R3 and R5 soybean stage on insect defoliation, insect pod and seed damage, and soybean grain yield. The experiment was conducted in 2022 and 2023 for a total of 10 site‐years in Ohio. The experimental design was a randomized complete block with four replications of each treatment. Treatments included foliar insecticide applied at the R3 soybean stage (beginning pod), insecticide applied at the R5 soybean stage (beginning seed), and a non‐treated control (no insecticide). Soybean leaf area affected by defoliation was evaluated the day of application and 2 weeks after application. At the R8 soybean stage (physiological maturity), soybean plants were collected and evaluated for insect pod damage and seed damage. The foliar insecticide application did not result in any significant change in soybean yield, likely explained by low insect defoliation and low pod damage. Prior to insecticide application, farmers should scout their fields and base decisions on integrated pest management strategies, considering threshold levels.

Genes of photoperiod sensitivity and early maturity E1-E4: dynamics of soybean growth in different daylength conditions

Background. Morphometric indicators are crucial for evaluating the development and productivity of soybeans. They are influenced by genetic and environmental factors. The use of nearly isogenic soybean lines is a convenient model for determining the impact of early maturity genes and daylength on growth indicators. The aim of this study was to determine the influence of different daylengths and early maturity genes on soybean morphometric indicators under conditions of the temperate zone (at the latitude of Kharkiv – 50° N). Materials and Methods. The study involved nearly isogenic soybean lines of the “Clark” cultivar with varying sensitivity to daylength. The research covers the results of field experiments over three seasons using different soybean lines. After reaching the V3 stage, some plants were subjected to short-day conditions for 14 days. Morphological indicators of ten plants per experimental variant were determined: plant height, dry weight, leaf number, and leaf surface area of soybean plants. Morphometric measurements were taken on the day of the beginning of different daylength treatments and on days 7, 14 and 21. The study results are presented as the mean values of the investigated parameters (plant height, dry weight, number of leaves and leaf surface area of soybean plants). Results. It was shown that under long-day conditions, dominant alleles of the E1 and E3 genes increased the dry weight of plants, while the dominant allele E2 increased plant height. The dominant allele E4 had no significant effect on plant height and weight indicators of soybean plants. Plants with dominant alleles of genes E1-E4 under long-day conditions had smaller leaf area compared to lines with recessive alleles of these genes. Conclusions. The obtained results on the relationships between genetic and environmental factors in influencing soybean plant height, weight, and leaf area can be useful in improving soybean yield and selecting cultivars that will be productive in high latitude conditions.

Heterodera glycines HG type 1.2.5.7 causes a decrease in soybean (Glycine max [L.] Merr.) nitrogen fixation and growth variables

ABSTRACT Soybean cyst nematode (SCN), Heterodera glycines, HG type 1.2.5.7 is the most virulent and widespread SCN biotype in the southern states of the USA, but its effect on soybean (Glycine max [L.] Merr.) nitrogen fixation (NF) activity and growth is not well documented. The objectives of this study were to: (1) measure the impact of HG type 1.2.5.7 on NF activity during the early vegetative stage of three soybean genotypes and (2) compare the impact on soybean leaf and root development, and plant growth. The NF activity of two susceptible (S) genotypes (“Ellis” and “Williams 82”) and one resistant (R) genotype (PI 88,788), subjected to a 10,000-egg treatment, was measured using an in-situ flow-through acetylene reduction assay. The average NF activity of the soybean genotypes was decreased by SCN activity. Time points assessment showed that the NF activity was differently impacted among the genotypes. Based on the measured values, the NF activity was decreased by 30% in SCN infected plants of Williams 82 but that of PI 88,788 and Ellis was affected. Likewise, soybean leaf color and leaf area were affected by SCN infection, but there was no impact on root length and root area. These results showed that SCN HG type 1.2.5.7 can exhibit varying negative impact on NF and shoot growth of S soybean genotypes, while R genotypes may be unaffected. Thus, indicating genetic resistance continues to be a critical tool for the management of the SCN.

Impact of defoliation at the reproductive stage in different leaves of the soybean plant

Inadequate phytosanitary management can cause a significant reduction in the leaf area of soybean, which directly impacts its yield. The aim of this study was to evaluate the impact of defoliation in the lower, middle and upper leaves of the canopy, at three reproductive stages, on soybean yield, as a tool for improving phytosanitary management. The design was randomized blocks in 3x3+1 factorial scheme, with 4 replicates. The 10 treatments resulted from the combination of 3 defoliation positions on the plant (lower, middle and upper leaves) and 3 phenological stages of the crop (R1, R3 and R5), in addition to a control without defoliation. Morphological and production components and grain yield were evaluated. The data were subjected to analysis of variance and, when a significant difference was identified, the means were subjected to the Dunnett test. It was observed that plants showed a reduction in height in treatments with defoliation in the upper leaves at R1 and R3 stages. For the variables thousand-grain weight and grain yield there was difference only when defoliation was performed at the R5 stage in the upper leaves, resulting in a 16.6% loss in grain yield compared to the control. The soybean crop tolerates a defoliation level of 33.3% up to the R4 stage, in any of the leaves, without significant reduction in grain yield. Upper leaves, from the R5 stage, should be prioritized in the applications of phytosanitary products, which aim to reduce the damage caused by defoliation.

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.

Weed detection in soybean fields using improved YOLOv7 and evaluating herbicide reduction efficacy.

With the increasing environmental awareness and the demand for sustainable agriculture, herbicide reduction has become an important goal. Accurate and efficient weed detection in soybean fields is the key to test the effectiveness of herbicide application, but current technologies and methods still have some problems in terms of accuracy and efficiency, such as relying on manual detection and poor adaptability to some complex environments. Therefore, in this study, weeding experiments in soybean fields with reduced herbicide application, including four levels, were carried out, and an unmanned aerial vehicle (UAV) was utilized to obtain field images. We proposed a weed detection model-YOLOv7-FWeed-based on improved YOLOv7, adopted F-ReLU as the activation function of the convolution module, and added the MaxPool multihead self-attention (M-MHSA) module to enhance the recognition accuracy of weeds. We continuously monitored changes in soybean leaf area and dry matter weight after herbicide reduction as a reflection of soybean growth at optimal herbicide application levels. The results showed that the herbicide application level of electrostatic spraying + 10% reduction could be used for weeding in soybean fields, and YOLOv7-FWeed was higher than YOLOv7 and YOLOv7-enhanced in all the evaluation indexes. The precision of the model was 0.9496, the recall was 0.9125, the F1 was 0.9307, and the mAP was 0.9662. The results of continuous monitoring of soybean leaf area and dry matter weight showed that herbicide reduction could effectively control weed growth and would not hinder soybean growth. This study can provide a more accurate, efficient, and intelligent solution for weed detection in soybean fields, thus promoting herbicide reduction and providing guidance for exploring efficient herbicide application techniques.

Effects of agricultural management and of climate change on N2O emissions in an area of the Brazilian Cerrado: Measurements and simulations using the STICS soil-crop model

Soil-plant simulation models have been ever more used as predictive tools to evaluate productivity and environmental impacts of agriculture practices. The aims of this work were to evaluate the STICS model’s capacity of simulate the emissions of nitrous oxide (N2O) under different management systems. And, to predict, by applying the calibrated and validated STICS model, the N2O emissions, under different management systems, in the edaphoclimatic conditions of the Brazilian Cerrado. The study was conducted in a long-term experiment at Embrapa Cerrados, located in Planaltina/DF, Brazil. The experiment started in 1995. The following land use systems were evaluated: CT - conventional tillage with disc harrow, and biannual grass/legume rotation; NT1 - No-tillage, with biannual crop rotation – soybean/sorghum; NT2 - No-tillage, with biannual crop rotation – maize/pigeon pea.Closed static chambers and gas sample analysis by gas chromatography were used to measure N2O fluxes. The model was calibrated with the data measured in the CT and NT1 treatments. The attributes used in the calibration were: growth, development and yield of crops and N2O fluxes. To test the model, data from the NT2 treatment was used. Data for all treatments were collected at the same time. After calibration and validation, the STICS model was used to predict the effects of soil management systems, conventional (CT) and no-tillage (NT) on greenhouse gas emissions and on grain and biomass crops production. The simulation was made for the period from 2021 to 2070. The STICS model performed well at simulation of the following parameters: N2O emissions, soil water dynamics, soil temperature; and leaf area, aerial biomass and grain yield of soybean and maize. The performance of the model was good for the conventional soil management system as well as in the no-tillage system. Based on the predicted increase in temperature for the period 2021–2070, we can conclude from the data generated through the STICS model that there is strong evidence that grain yield and total aerial biomass of plants will decrease. The data show that there is a rising tendency in N2O emissions over the simulated period. This is expected to occur for the two soil management systems studied (NT and CT). We believe this increase in emissions along the years is related to temperature increase and to the reduction of crop cycle.

Use of Visible Spectral Index and Soybean Plant Variables to Study Hidden Nematicide Phytotoxicity

Significant crop losses are due to plant-parasitic nematodes. Nematicides are expensive and potentially toxic to men, the environment, and plants. This study evaluated the hidden phytotoxicity effects of nematicides in soybeans. Two soybean cultivars (8473RSF and M7198IPRO) were evaluated with five nematicide treatments (biological, cadusaphos, abamectin, fluensulfone, and an untreated control) for changes in chlorophylls, biometrics, and spectral (TGI visible spectral index captured with a smartphone camera) variables to determine and anticipate the identification of plant stresses. Evaluations occurred 33, 47, and 66 days after sowing (DAS). The a/b chlorophyll proportion was greatest for M7198IPRO and cadusaphos. The chlorophyll variables did not present significant interactions or differences at 47 DAS, indicating that possible nematicide effects were transient and should be evaluated earlier than 33 DAS. Leaf area, leaf mass, and shoot mass were smaller for 8473RSF and outstanding for abamectin and fluensulfone. The response of the spectral index did not present significant interaction among the factors; however, at 33 and 47 DAS, the index was low for 8473RSF and lowest for cadusaphos only at 33 DAS. The correlations between the spectral index and other variables were significant and moderate for soybean total leaf area. Although no apparent phytotoxicity symptoms caused by nematicides were observed, the visible vegetation index generated using a smartphone camera can still improve crop management solutions.

Modeling the early phases of epidemics by Phakospora pachyrhizi in Brazilian soybean

Asian soybean rust, caused by the biotrophic basidiomycete Phakospora pachyrhizi, is a foliar disease that often causes considerable damage to soybean crops. The purpose of our work was to create a mechanistic model that can reliably represent epidemics of ASR in commercial soybean fields in Brazil. The most important inputs for the model are weather data (observations and forecast) and the initial observation of disease (or uredospore arrival). Our focus is on the first two or three cycles of infection after immigration into a soybean field. The model includes state variables for latent, infectious and senesced lesions, disease severity, uredospores, and soybean leaf area. Processes modeled include maturation through the latent and infectious periods, germination, sporulation, and processes affecting uredospores in the canopy. The model results were tested against field observations from trials at four locations in Brazil for the 2019/2020 growing season. The predictions generally matched the daily dynamics of disease progress in the field trials. The predictions reproduced the observed severity well with R2 value of 0.84. This high correlation indicates that our model is accurate enough to be used as a tool to predict the dynamics of ASR epidemics during the first few cycles after uredospore invasion into a soybean field. A sensitivity analysis was performed that showed that the model is sensitive to time and duration of the initial spore arrival. This indicates that spore traps or other observations should measure not only the first day of arrival but also subsequent days.

PENGARUH PEMBERIAN KALIUM TERHADAP FISIOLOGIS DAN MORFOLOGIS KEDELAI PADA CEKAMAN KEKERINGAN

Domestic soybean production is weakened by the problem of drought stress that affects all aspects of plant growth and metabolism, while an alternative that can be used is the use of potassium. Potassium is believed to have an important role in increasing plant tolerance to drought. This study aims to examine the effect of potassium application on the physiological and morphological of drought-stressed soybean. The research was conducted in a plastic house in Aras Kabu Village, Beringin Subdistrict, Deli Serdang Regency, North Sumatra using a group randomized design with two factors and three replications. The first factor was soil moisture condition consisting of 80% KL (field capacity) and 40% KL. The second factor was potassium fertilizer application of 100 kg/ha, 200 kg/ha, 300 kg/ha, and no potassium fertilizer. The observed variables were total sugar and leaf area. The results showed that the use of potassium can increase the value of total sugar and leaf area of drought-stressed soybean. Application of K dose of 100 kg/ha (0.5 g/polybag) is the most appropriate to encourage physiological and morphological drought stressed soybean plants.

Coordinated responses of leaf and nodule traits contribute to the accumulation of N in relay intercropped soybean

Maize (Zea mays L.)–soybean (Glycine max L. Merr.) relay intercropping provides a way to enhance land productivity. However, the late-planted soybean suffers from shading by the maize. After maize harvest, how the recovery growth influences the leaf and nodule traits remains unclear. A three-year field experiment was conducted to evaluate the effects of genotypes, i.e., supernodulating (nts1007), Nandou 12 (ND12), and Guixia 3 (GX3), and crop configurations, i.e., the interspecific row spacing of 45 (I45), 60 (I60), 75 cm (I75), and sole soybean (SS), on soybean recovery growth and N fixation. The results showed that intercropping reduced the soybean total leaf area (LA) by reducing both the leaf number (LN) and unit leaflet area (LUA), and it reduced the nodule dry weight (NW) by reducing both the nodule number (NN) and nodule diameter (ND) compared with the SS. The correlation and principal component analysis (PCA) indicated a co-variability of the leaf and nodule traits in response to the genotype and crop configuration interactions. During the recovery growth stages, the compensatory growth promoted soybean growth to reduce the gaps of leaf and nodule traits between intercropping and SS. The relative growth rates of ureide (RGR_U) and nitrogen (RGR_N) accumulation were higher in intercropping than in SS. Intercropping achieved more significant sucrose and starch contents compared with SS. ND12 and GX3 showed more robust compensatory growth than nts1007 in intercropping. Although the recovery growth of relay intercropping soybean improved biomass and nitrogen accumulation, ND12 gained a more significant partial land equivalent ratio (pLER) than GX3. The I60 treatment achieved more robust compensation effects on biomass and N accumulation than the other configurations. Meanwhile, I60 showed a higher nodule sucrose content and greater shoot ureide and N accumulation than SS. Finally, intercropping ND12 with maize using an interspecific row spacing of 60 cm was optimal for both yield advantage and N accumulation.

Flavonoids and Devosia sp SL43 cell-free supernatant increase early plant growth under salt stress and optimal growth conditions.

Salt stress is a major threat to modern agriculture, significantly affecting plant growth and yield, and causing substantial economic losses. At this crucial time of increasing climate change conditions, soil salinity will continue to develop and become an even more serious challenge to crop agriculture. Hence, there is a pressing need for sustainable techniques in agricultural production that could meet the dual challenges of crop productivity and environmental instability. The use of biostimulants in agricultural production has greatly influenced plant health and global food production. In particular, the application of bioactive materials produced by beneficial microbes is becoming a common practice in agriculture and provides numerous benefits to plant growth and resistance to stressful conditions. In this research two biostimulants; a type of plant secondary metabolite (flavonoids) and a microbe-based material (CFS: Cell-Free Supernatant) containing active compounds secreted by a novel bacterial strain isolated from Amphecarpaea bracteata root nodules (Devosia sp - SL43), have been utilized to improve the growth and stress resistance of two major oil seed crops; canola and soybean, under optimal and salt stress conditions. Our findings suggested significant improvements in crop growth of canola and soybean following the application of both biostimulants. Under optimal growth conditions, soybean growth was significantly affected by foliar spray of flavonoids with increases in shoot fresh and dry weight, and leaf area, by 91, 99.5, and 73%, respectively. However, soybean growth was unaffected by flavonoids under salt stress. In contrast, CFS with a meaningful capacity to mitigate the negative effects of salinity stress improved soybean shoot fresh biomass, dry biomass, and leaf area by 128, 163 and 194%, respectively, under salt stress conditions. Canola was less responsive to both biostimulants, except for canola root variables which were substantially improved by flavonoid spray. Since this was the first assessment of these materials as foliar sprays, we strongly encourage further experimentation to confirm the findings reported here and to determine the full range of applicability of each of these potential technologies.

Area estimation of soybean leaves of different shapes with artificial neural networks

Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.

Morphological and chemical plant traits associated with feeding non-preference to adult of Diabrotica speciosa (Coleoptera: Chrysomelidae) in soybean genotypes.

Diabrotica speciosa is an important pest of several crops in South America, including soybeans. Adults cause severe defoliation in soybean plants, and damage is significant when cotyledons are attacked. This study evaluated feeding non-preference to D. speciosa adults using 10 soybean genotypes, testing (i) 15-day-old whole plants and (ii) leaf disks of 60-day-old plants, through assessments of soybean attractiveness and leaf area consumed (LAC). Foliar contents of flavonoids and nutrients, and leaf trichome density were quantified for potential correlations with soybean resistance to adult of D. speciosa. In the whole young-plant experiment, under free-choice conditions, the lowest LAC was observed in IAC 100 and PI 227687. In no-choice, PI 227687 and IGRA RA 626 RR showed lower LAC than the other genotypes. In the leaf disk test, in free-choice, the genotypes IAC 100, PI 274454, PI 227687, DM 339, and BR 16 were the least preferred by adult of D. speciosa. In no-choice, PI 274454 was one of the least preferred, similarly to IGRA RA 626 RR, Dowling, and PI 227687. In the whole plant experiment, a high rutin content and low amounts of zinc, calcium, sulfur and manganese were associated with less consumption of D. speciosa on leaves of resistant genotypes. In contrast, in the leaf disk test there was a significant influence of trichomes in soybean resistance to the pest. In conclusion, the PI lines herein assessed are also promising sources for developing cultivars resistant to D. speciosa.

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.

ОСОБЕННОСТИ РОСТА И РАЗВИТИЯ СОРТОВ СОИ ПРИ ВОЗДЕЛЫВАНИИ НА ОРОШАЕМЫХ ЗЕМЛЯХ РОСТОВСКОЙ ОБЛАСТИ

Purpose: selection of soybean varieties adapted to the soil and climatic conditions of Rostov region, taking into account the cultivation technology application on irrigated lands. Materials and methods. Three-year studies were conducted in Oktyabrsky district Rostov region on irrigated lands. Soils under soybean crops are meadow-chernozem heavy loamy. In the experiments, 24 soybean varieties of different ripeness groups were studied. The experiment was carried out in four repetitions with a randomized arrangement of plots. Observations of the soybeans growth and development, yield records, statistical processing of data were carried out according to generally accepted methods of field experiments. Results. The duration of vegetation of the studied soybean varieties ranged from 100 to 125 days. Observations of the growth and development of plants made it possible to determine that, depending on the variety, the height of soybean plants reached 82.9–133.0 cm in the bean-filling phase; during this period, the maximum values of the soybean leaf area duration vary from 38.68 to 84.32 thousand sq. m/ha, and the dry biomass of soybeans varied from 1.31 to 2.31 t/ha. The productivity indicators of the studied soybean varieties on irrigation have been determined. The soybean variety yield varied from 3.19 to 5.34 t/ha. The maximum value was recorded for the variety SK Elana, the minimum was for the variety Kazachka. An analysis of the qualitative composition of soybean grain made it possible to find that the protein content varied from 37.4 to 44.0 %, and the protein harvest was from 1.1 to 1.9 t/ha. The oil content varied from 17.8 to 22.9 % depending on the variety, and its harvest from 0.5 to 1.0 t/ha. Conclusions. Soybean cultivation on irrigated lands allows to obtain high productivity of a wide range of varieties. The maximum yield of soybean grain and protein harvest were obtained from the SK Elana variety – 5.34 t/ha and 1.9 t/ha, respectively. The maximum oil content in the grain was obtained from the Selecta 302 variety – 22.9 %, and its harvest from the SK Elana variety was 1.0 t/ha.

Water deficit and plant recovery affect interaction between soybean and slender amaranth

Biotic and abiotic factors such as competition with weeds and water deficit may cause significant losses to soybean productivity. This study aimed at evaluating the effect of water deficit and plant recovery in the interaction between soybean (Glycine max) and slender amaranth (Amaranthus viridis) by using growth variables. A replacement series experiment was performed and treatments were arranged in a factorial scheme; they were composed of two water regimes (field capacity and water deficit (50% of field capacity)) and different soybean and slender amaranth proportions (100:0, 50:50 and 0:100%). Water regimes were applied 20 days after plant transplant. Height, leaf area and shoot dry matter were evaluated in soybean and slender amaranth under field capacity and water deficit, returning to normal irrigation conditions to evaluate the water recovery. Soybean exhibited competitiveness which was equivalent to the slender amaranth, independent of the water regime. Water deficit negatively influenced height, leaf area and shoot dry matter of soybean and slender amaranth. There was a reduction in leaf area and shoot dry matter of soybean after plant recovery caused by the irrigation deficit period.

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.

Digital processing of leaf area of soybeans, subjected to different treatments of seeds

Digital image processing, when applied to the study of leaf area, allows the integration of the direct measurement and non-destructive, and thus preserves the integrity of the plant. The objective was the quantification of the leaf area of soybean, cv. FTS Paragominas RR, submitted to different treatments of seed with the use of the computer program ImageJ, and basic presuppositions of image processing. The experiment was conducted at the Center of Agrarian Sciences and Environmental, Federal University of Maranhão, in Chapadinha (MA), in the period from February to June 2018. The seeds of soybean 'Paragominas RR' were submitted to the technique of seed treatment, consisting of three fungicides of the active ingredients, thiophanate methyl + fluazinam, fludioxonil and carbendazim + tiram, an insecticide active ingredient fipronil and the control. The leaf area was analyzed in the growth phase, through the use of digital camera and ImageJ®. The use of the routines in the computer program ImageJ® were effective for the determination of leaf area of the soybean submitted to different treatments of the seed. The thiophanate methyl + fluazinam in the dose 200 mL per 100 kg of seeds showed beneficial effects on growth of the cv. FTS Paragominas RR, as estimated by the leaf area.

Effect of commercial arbuscular mycorrhizal fungi inoculant on growth and yield of soybean under controlled and natural field conditions

Pot and field experiments were conducted to assess varietal differences in growth and yield responses of soybean (Glycine max L. Merrill) to commercial arbuscular mycorrhizal fungi (AMF) inoculant (Empathy Mycorrhizal Rootgrow™). Three soybean cultivars (TGx 1448-2E, TGx 1440-1E and TGx 1740-2F) were grown under two levels of AMF inoculation (with and without), replicated three times in both experiments. The study showed that mycorrhizal inoculation significantly increased plant height, stem girth, number of leaves and leaf area of soybean of the three cultivars compared to AMF-free control in both experiments. This resulted in higher yield attributes and yield of soybean. AMF root colonization ranged from 56% - 76% in the cultivars. TGx 1448-2E performed better producing highest yield attributes and yield than TGx 1440-1E or TGx 1740-2F. It could be concluded from this study that the use of the commercial AMF inoculant has the potential to promote soybean growth and yield under favorable conditions.