Soybean Yield Research Articles

Impact of Rhg1 copy number variation on a soybean cyst nematode resistance transcriptional network.

Soybean yield loss due to soybean cyst nematode (SCN) infestation has a negative impact on the U.S. economy. Most SCN-resistant soybeans carry a common resistance locus (Rhg1), conferred by copy number variation of a 31.2-kb segment at the Rhg1 locus. To identify the effects of Rhg1 copy number on the plant prior to SCN infection, we investigated genome-wide expression profiles in isogenic Fayette plants carrying different copy numbers at the Rhg1 locus (9-11 copies), that confer different levels of resistance to SCN. We found that even small differences in copy number lead to large changes in expression of downstream defense genes. The co-expression network constructed from differentially expressed genes (DEGs) outside the Rhg1 locus revealed complex effects of Rhg1 copy number on transcriptional regulation involving signal transduction and ethylene-mediated signaling pathways. Moreover, we report a variation in expression levels of phytoalexin biosynthesis-related genes that is correlated with copy number, and the activation of different NBS-LRR gene sets, indicating a broad effect of copy number on defense responses. Using qRT-PCR time series during SCN infection, we validated the SCN responses of DEGs detected in the copy number comparison and showed a stable upregulation of genes related to phytoalexin biosynthesis in resistant Fayette lines during the early stages of the incompatible interaction between soybeans and SCN, before syncytium formation. These results suggest additional genes that could enhance Rhg1-mediated SCN resistance.

Plant growth-promoting microorganisms drive K strategists through deterministic processes to alleviate biological stress caused by Fusarium oxysporum

Soybean root rot, caused by soil-borne pathogens such as Fusarium oxysporum, frequently occurs in Northeast China and leads to a decline in soil health and becoming a bottleneck for soybean yield in the region. To address this issue, applying beneficial microorganisms and altering soil microbial community structure have become effective strategies. In this study, the 90-day soybean pot experiment was conducted to explore the assembly process and life strategy selection of bacterial communities in the rhizosphere of healthy (inoculated with Funneliformis mosseae, F group and treated with Pseudomonas putida, P group) and diseased (inoculated with F. oxysporum, O group) soybean plants, as well as the recovery effect of beneficial microorganisms on soil-borne diseases (combined treatments OP and OF). Results indicated that in healthy soils (P and F), microbial community assembly process in the soybean rhizosphere was entirely governed by heterogeneous selection (HeS, 100 %). However, inoculated with P. putida (OP) was primarily driven by stochastic processes (HeS 40 %, dispersal limitation (DL) 60 %), and the F. mosseae treatment (OF) predominantly followed a deterministic process (HeS 89 %, DL 11 %) in diseased soils. Inoculation of plant growth-promoting microorganisms (PGPMs) in diseased soil drove the life strategy of the rhizosphere bacterial community from r- to K-strategy, evident from the lower rRNA operon (rrn) copy numbers (O 3.7, OP 2.1, OF 2.3), higher G+ to G- ratios (O 0.47, OP 0.58, OF 0.57), and a higher abundance of oligotrophs (O 50 %, OP 53 %, OF 54 %). In healthy (P and F) and diseased (O, OP, OF) rhizosphere soils, OTU820, OTU6142, and OTU8841 under the K-strategy, and OTU6032 and OTU6917 under the r-strategy, which served as keystone species, had a significant promoting relationship with plant biomass and defense capabilities ( p ＜0.05). Additionally, inoculation of PGPMs improved autotoxin degradation and positively correlated with bacterial life strategies in both healthy and diseased soils (P, F, OP and OF) ( p ＜0.05). These findings enhance our understanding of soil-microbe interactions and offer new insights and precise control measures for soybean disease management and soil environment remediation.

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

Soybean stand and yield impact from a fungicide seed treatment in North Carolina soybean production

AbstractNorth Carolina soybean [Glycine max (L.) Merr.] producers have shifted to earlier planting dates as a mechanism to increase soybean yields. As growers have shifted to earlier planting dates, soybean fungicide seed treatment use has become more common. In 2020 and 2021, on‐farm research investigated the value of a fungicide seed treatment across the state. Experiments were conducted across 18 diverse production environments in North Carolina during that time. Treatments included fungicide seed treatment (fludioxonil: 2.32%, mefenoxam: 13.9%, and sedaxane: 2.32%, i.e., Vibrance Trio, a commonly used multi‐mode of action fungicide across the state and country) compared to untreated seed. Data collected included soybean stand and soybean yield. The use of a fungicide seed treatment variably affected stand, but when combined over environments, fungicide seed treated plots averaged 110,757 plants/acre as opposed to untreated plots averaging 101,570 plants/acre with significant stand protection in seven of 18 environments. When averaged across environments, fungicide seed treatment protected yield by 1.3 bu/acre which, depending on input practices and soybean selling price, would border providing a return on investment. As planting date was delayed past mid‐April, yield decreased for both fungicide treated and untreated plots. Results from this trial indicate that fungicide seed treatments can provide protection of soybean yield and stand in North Carolina although protection provided may not provide a return on investment with current input prices.

Effect of Novel Biostimulants on Performance of Black Gram and Soybean in Peninsular India

ABSTRACT The objective of modern agriculture includes environmental sustainability, low production costs, and higher productivity. Biostimulants are composed of humic acids, hormones, algae extracts, plant growth-promoting bacteria, amino acids, and a chain of peptides. Black gram and soybean crops are of immense economic significance. The growth and yield of these crops are limited by supply of nutrients. The potentiation of the early growth of plants by treating seeds with these substances has been one of the adopted alternatives, which often promote physiological changes that stimulate growth. The field experiment was conducted with randomized complete block design and replicated four times. Effects of different biostimulants at different concentrations as foliar spray and seed treatment were examined on growth, yield parameters, and yield of black gram (TAU-1) and soybean (JS-335) during kharif 2020 and 2021. Results showed that foliar application of Quantis biostimulant @ 5 ml L−1 recorded higher growth, yield parameters, and seed yield (897 kg ha−1), biological yield (1209 kg ha−1), production efficiency (13.00 kg day−1 ha−1) in black gram and seed treatment with Epivio Energy @ 2 ml kg−1 fetched higher seed yield (1403 kg ha−1), biological yield (2914 kg ha−1), production efficiency (16.13 kg day−1 ha−1) along with higher growth and yield parameters in soybean. It can be concluded that they will help in achieving higher growth and yield of black gram and soybean.

Yield and profit comparison of diversified versus conventional crop rotation systems in South Dakota

AbstractDiversified crop rotations with no‐till management are considered fundamental to sustainable agroecosystems; however, associated uncertainty in economic returns may hinder farmers’ decisions to adopt these practices. The objectives of this study were to compare economic performance (gross revenue, net revenue, and production cost) among various low‐input diversified versus conventional crop rotations. The experiment was initiated in the fall of 2000 with winter wheat (Triticum aestivum L.) planting and planting of remaining crops in the spring of 2001 near Brookings, SD. Economic analysis was performed from data collected during the fifth complete 4‐year crop rotational cycle (2017–2020) of six crop rotations: (1) 4‐year corn (Zea mays L.)‐soybean [Glycine max L. (Merr.)]‐spring wheat‐sunflower (Helianthus annus L.) (CSSwSf), (2) 4 year corn‐soybean‐spring wheat‐pea (Pisum sativum L.) (CSSwP), (3) 4‐year corn‐pea‐winter wheat‐soybean (CPWwS), (4) 4‐year corn‐oat (Avena sativa L.)‐winter wheat‐soybean (COWwS), (5) 2‐year corn‐soybean (CS), and (6) continuous corn (CCC, treatment initiated in 2017). Corn yield in CSSwP rotation, where corn followed peas, was higher (p < 0.05) by 20%, 25%, 45%, and 89%, compared to CPWwS, CSSwSf, CS, and CCC rotations, respectively. Similarly, soybean yield following winter wheat was significantly higher by 16%–38% in COWwS and 13%–38% in CPWwS compared to CSSwP, CSSwSf, and CS. Overall, diversified crop rotations improved both corn and soybean yield and net revenue compared to 2‐year CS and monoculture CCC rotations. Moreover, 4‐year diversified systems, specifically COWwS, CPWwS, and CSSwP, demonstrated economic resilience by maintaining stable production costs.

Evaluating the impact of phyto-hormones on the morpho-biochemical traits of soybean through seed treatment and foliar application

Global climate variations, mainly extreme temperatures reduce crop production and contributed to global food insecurity. Plant growth regulators (PGRs) improve physiological efficiency, photosynthetic capacity, and assimilate partitioning in field crops. However, their influence on soybean (Glycine max L.) yield and seed quality is under-researched. This study investigates the synergistic effects of auxin (2,4-D) and gibberellin (GA3) at 60 and 90 mg L−1 concentrations. This study investigates the impact of phytohormones, specifically gibberellic acid (GA3) and 2,4-dichlorophenoxyacetic acid (2,4-D), on the morphological and biochemical traits of soybean (Glycine max L.) through seed treatment and foliar application. These Growth regulators were applied through seed treatment at the time of sowing and foliar application at the time of flowering initiation stage. The experiment was designed using a RCBD arrangement and each block replicated thrice time. Seed treatment with 2,4-D at 90 mg/L showed that maximum plant height (116.80 cm), first node height (8.04 cm), No. of branches per plant (18.67), root length (18.12 cm) stem diameter (0.05 cm), no. of pods per plant (32.60), seed yield (1532.30 kg ha−1), biological yield (4109.30 ha−1), and harvest index (39.36 %). The GA3 (seed treatment) at conc. of 90 and 60 mg L−1 resulted in a significantly increased no. of nodule fresh weight, nodules per plant, and dry weight respectively. But the no. of seeds per pod did not showed significant results throughout the study. However, the foliar applied of 2,4-D at 90 mg L−1 resulted in significantly increased leaf area index (LAI) compared to the seed treatment. PGRs had a significant influence on quality traits. Thus, the application notably improved the potential of soybean and successful oilseed crop. Therefore, it is showed that using PGRs are extremely helpful for attaining higher growth, yield production, and improved quality of soybean.

Simulation of the soybean yield as a function of water sensitivity in rainfed cultivation using the CROPWAT model

Simulation of the soybean yield as a function of water sensitivity in rainfed cultivation using the CROPWAT model

Effect of nozzles on disease control with spraying fungicides in soybean crops

ABSTRACT The productivity of the soybean crop (Glycine max) can be limited by several factors, including diseases. In the integrated management of diseases, one of the strategies used is chemical control. The aim of this experiment was to evaluate the influence of spray nozzle types on the chemical control of fungal diseases in soybean plants. The experimental design was randomised blocks, with six treatments and four replications. The treatments consisted of control (without fungicide application on soybean plants) and application of fungicides with different spray nozzles: hollow cone jet, double flat jet, extended range flat jet, wide angle flat jet and air induction flat jet. The sprayer used was the backpack pressurised by carbon dioxide CO2. The experiment was installed in the Campos Gerais region (Paraná, Brazil); during three cropping seasons. The evaluated variables were incidence and severity, spray quality (covered area, droplet density, and relative amplitude) and yield components. It was concluded that the chemical control of fungal diseases in soybean plants reduced the proliferation of pathogens and losses in crop yield. There were significant changes in spraying quality in all analysed variables. There were no differences among the spray nozzle types regarding the analysis of incidence, severity and soybean yield components.

Molecular Regulation of Shoot Architecture in Soybean.

Soybean (Glycine max [L.] Merr.) serves as a major source of protein and oil for humans and animals. Shoot architecture, the spatial arrangement of a plant's above-ground organs, strongly affects crop yield and is therefore a critical agronomic trait. Unlike wheat and rice crops that have greatly benefitted from the Green Revolution, soybean yield has not changed significantly in the past six decades owing to its unique shoot architecture. Soybean is a pod-bearing crop with pods adhered to the nodes, and variation in shoot architecture traits, such as plant height, node number, branch number and number of seeds per pod, directly affects the number of pods and seeds per plant, thereby determining yield. In this review, we summarize the relationship between soybean yield and these major components of shoot architecture. We also describe the latest advances in identifying the genes and molecular mechanisms underlying soybean shoot architecture and discuss possible directions and approaches for breeding new soybean varieties with ideal shoot architecture and improved yield.

Integrated Crop–Livestock Systems as a Strategy for the Sustainable Production of Corn and Soybean Grain in Tropical Sandy Soils

Integrated crop–livestock systems (ICLS) have sustainably intensified modern agricultural practices worldwide. This research assessed how production systems and crop types impact the chemical properties of an Oxisol in the Brazilian Cerrado, the grain yield of corn intercropped with palisade grass (Urochloa) in the off-season in an ICLS, and the grain yield (GY) of soybean in succession. Intercropped and monocropped systems were assessed in a three-year field experiment: corn + Urochloa ruziziensis–soybean; corn + U. brizantha cv. Piatã–soybean; corn + U. brizantha cv. Paiaguás–soybean (ICL–Paiaguás); corn–soybean under a no-tillage system (NTS); corn–soybean under a conventional tillage system (CTS); Piatã grass–continuous grazing (Perennial Piatã); and Paiaguás grass–continuous grazing (Perennial Paiaguás). The residual impact of phosphate fertilization was more pronounced in the ICLS treatments. In the soil layer from 0.0 to 0.2 m depth, ICLS–Paiaguás and Perennial Piatã had the most positive effects on soil chemical quality. In the last year, grain yield was highest in corn monoculture under the NTS and soybean in succession under the ICLS. ICL–Paiaguás improved soil chemical properties for soybean in succession. These results confirm that an intermittent pasture system for legume crops in sequence is an alternative that can maintain or improve soil chemical composition, and that CTS should be avoided in tropical sandy soils.

Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.

Growth promotion and modulation of the soybean microbiome INTACTA RR PRO with the application of the fungi Trichoderma harzianum and Purpureocillum lilacinum

Soybean is an economically important crop for animal and human nutrition. Currently, there is a lack of information on the effects of Trichoderma harzianum and Purpureocillum lilacinum on INTACTA RR PRO transgenic soybean plants. The present study evaluated the application of T. harzianum and P. lilacinum under field conditions. The results revealed a significant increase in soybean yield at 423 kg ha−1 in response to the application of P. lilacinum compared with the control treatment. In addition, the application of P. lilacinum promoted a significant increase in phosphorus levels in the plant leaves, and there were significant correlations between the increase in taxon abundance for the genus Erwinia and productivity and the average phosphorus and nitrogen content for the plant leaves, for the taxon Bacillus and nitrogen content and productivity, and for the taxon Sphingomonas and nitrogen content. The Bradyrhizobium taxon was identified in the P. lilacinum treatment as a taxon linking two different networks of taxa and is an important taxon in the microbiota. The results show that the application of the fungus P. lilacinum can increase the productivity of soybean INTACTA RR PRO and that this increase in productivity may be a function of the modulation of the microbiota composition of the plant leaves by the P. lilacinum effect.

Soybean Yield Simulation and Sustainability Assessment Based on the DSSAT-CROPGRO-Soybean Model.

In order to ensure national grain and oil security, it is imperative to expand the soybean planting area in the Xinjiang region. However, the scarcity of water resources in southern Xinjiang, the relatively backward soybean planting technology, and the lack of a supporting irrigation system have negatively impacted soybean planting and yield. In 2022 and 2023, we conducted an experiment which included three irrigation amounts of 27 mm, 36 mm, and 45 mm and analyzed the changes in dry mass and yield. Additionally, we simulated the potential yield using the corrected DSSAT-CROPGRO-Soybean model and biomass based on the meteorological data from 1994 to 2023. The results demonstrated that the model was capable of accurately predicting soybean emergence (the relative root mean square error (nRMSE) = 0, the absolute relative error (ARE) = 0), flowering (nRMSE = 0, ARE = 2.78%), maturity (nRMSE = 0, ARE = 3.21%). The model demonstrated high levels of accuracy in predicting soybean biomass (R2 = 0.98, nRMSE = 20.50%, ARE = 20.63%), 0-80 cm soil water storage (R2 = 0.64, nRMSE = 7.78%, ARE = 3.24%), and yield (R2 = 0.81, nRMSE = 10.83%, ARE = 8.79%). The biomass of soybean plants increases with the increase in irrigation amount. The highest biomass of 63 mm is 9379.19 kg·hm-2. When the irrigation yield is 36-45 mm (p < 0.05), the maximum yield can reach 4984.73 kg·hm-2; the maximum efficiency of soybean irrigation water was 33-36 mm. In light of the impact of soybean yield and irrigation water use efficiency, the optimal irrigation amount for soybean cultivation in southern Xinjiang is estimated to be between 36 and 42 mm. The simulation results provide a theoretical foundation for soybean cultivation in southern Xinjiang.

Effects of Native Rhizobia on Soybean [Glycine max (L.) Merrill] Production and Soil Properties in Daloa, Center-West of Côte d’Ivoire

The field experiment study was conducted at the experimental station of Jean Lorougnon Guede University in Daloa to evaluate the potential effects of native rhizobia strains on soybean production and soils properties. The experiment was layout in a randomized complete block design with three replications. The treatments include seven native strains, one exotic strain (Bradyrhizobium japonicum IRAT FA3), one synthetic fertilizer NPK 12-22-22 formulation and one negative control (no fertilizer, no inoculation). Agronomic parameters (plant height, plant dry biomass, nodule number plant-1, pod number plant-1, pod weight plant-1, seed yield ha-1) and soil parameters (pH, total nitrogen, orgnanic carbon, organic matter and available phosphorus) were measured. Results showed that all tested native rhizobia enhanced nodules number, plant growth and soybean yield as compared to negative control. Among these native rhizobia, RSC119 and RSC504 induced more nodules than the introduced strain B. japonicum IRAT FA3. RSC119, RSC309 and RSC508 produced more pods and seed yield than the introduced strain B. japonicum IRAT FA3 and the synthetic fertilizer NPK 12-22-22. RSC508 induced the highest pods number plant-1 (102 pods) and seed yield (4.2 t.ha-1). Moreover, these local bacteria had positive effects on soil properties following the soybean’s harvest. Among these bacteria, RSC119 enhanced mostly total nitrogen, organic matter and available phosphorus, then RSC508 significantly reduced soil acidity. This study therefore suggests that local rhizobia are effectiveness and could be use as inoculum to improve soybean productivity and soil properties restoration.

Soybean prediction using computationally efficient Bayesian spatial regression models and satellite imagery

AbstractPreharvest yield estimates can be used for harvest planning, marketing, and prescribing in‐season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non‐Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced‐based blue, green, red, and near‐infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP; R2‐testing = 0.485) model, which captures the short‐range correlation, outperformed the (BLR; R2‐testing = 0.02), Bayesian spatial regression (SRM; R2‐testing = 0.087), and Bayesian skewed spatial regression (sSRM; R2‐testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within‐field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lower R2 values and higher execution times than the random forest model.

Soil Physical and Chemical Properties under Shea Tree (Vitellaria paradoxa) at Different Stages of Growth

Shea tree (Vitellaria paradoxa), is one of the dominant agro-forestry species in Otuke district of Northern Uganda. Due to its economic importance and, in line with the numerous threats the tree is faced with, there is an urgent need for measures to conserve this species, for example, through incorporating annual food crops in the Shea tree parkland. This, however, requires a better understanding of tree-soil-food crop interactions. A number of studies of this aspect either considered only the mature Shea tree gardens or did not provide a clear distinction between the physiological states of the Shea tree. This was the motivation for this study where we compare variation in soil properties under mature and young Shea tree gardens with sites not having trees in Okwang sub-county, Otuke district. Five soil samples (up to 15 cm deep for top soil and 15-30 cm for sub-soil) were obtained per treatment using a soil auger. Our results show that in the top soil, only percent sand varied among the treatments, while, in the sub-soil, only percentage nitrogen and average phosphorus varied among the treatments. We also found that percentage top soil organic matter and percentage of sub-soil sand had negative strong correlations with maize and soybean yields, while percentage sub-soil clay had a strong positive correlation with maize and soybean yield. We conclude that variations in soil physical and chemical properties under Mature and Young Shea gardens only occur for those properties that have a direct link to tree residues

How Do Off-Season Cover Crops Affect Soybean Weed Communities?

Weeds compete for environmental resources, leading to reduced soybean yield. In this context, integrated weed management strategies related to cultural control with the use of cover crops are necessary. Our aim was to evaluate weed occurrence in soybean systems with different cover crops. Field studies were conducted at Júlio de Castilhos, Santa Maria, Capão do Leão, Barra do Ribeiro, and Santo Ângelo, Rio Grande do Sul, Brazil. A randomized complete block design with four replications was used. Treatments consisted of black oats (Avena strigosa Schreb.), white oats (Avena sativa L.), rye (Secale cereale L.), hairy vetch (Vicia sativa L.), forage turnip (Raphanus sativus L.), and white clover (Trifolium repens L.) in pure stands or in mixtures. The analyzed variables were relative frequency, density, abundance, and importance value index, similarity index of weeds, dry shoot mass of cover crop, and soybean yield. Cover crops containing white or black oats reduced the relative importance value index of weeds, such as Lollium multiforum, Conyza spp., and Bidens pilosa. Forage turnip, hairy vetch, and white clover showed distinct responses. Black oats and forage turnip did not differ from cover crop mixtures in terms of dry shoot mass and grain yield, being superior to fallow, white clover, and hairy vetch.

A Comparative Study of Long-Term Conventional and No-Tillage Practices on the Basis of Available Soil Nutrients, Soil Organic Carbon and Crop Productivity in Black Soils of Central India

Crop residue removal in conventional tillage (CT) can cause the depletion of soil nutrients (such as N, P, K) and organic carbon resulting in negative nutrient balance/depleted soil fertility. No-tillage (NT) is seen as a good substitute for CT in terms of preserving soil fertility and enhancing the soil productivity. The present study carried out in black soil of central India comprising of 2 tillage systems and 3 crop rotations to compared the effects of long-term conventional and no-tillage practices on available soil nutrients, soil organic carbon and crop yield differences in different cropping systems. Conventional tillage and No-till were factored into, soybean-wheat, maize-wheat and maize-gram systems. The long-term no-tillage treatment resulted in higher soil organic carbon (0.95%), available soil nitrogen (222.61 Kg ha-1), phosphorus (24.62 Kg ha-1) and potassium (583.63 Kg ha-1) contents at the 0–10 cm depth than the conventional tillage treatment. Crop productivity in terms of soybean grain equivalent yield (SGEY) was significantly higher in NT (41.42 quintal ha-1) compare to CT (35.36 quintal ha-1). The study proven that no-tillage is an effective strategy to improve soil fertility (organic carbon and available nutrients) and crop yield of different cropping systems in black soils of central India.

Utilization of natural hybridization and intra‐cultivar variations for improving soybean yield, seed weight, and agronomic traits

AbstractSoybean (Glycine max) is a highly self‐pollinated species, but cross‐pollination occasionally occurs and variations within cultivars can be observed under certain conditions. To explore the potential uses of natural hybridization and intra‐cultivar/advanced line variations, 78 of breeding lines derived from the segregants of natural hybridization and the intra‐cultivar/line variations and their 17 source cultivars/lines were evaluated over four crop seasons for yield, seed weight, and other agronomic traits. All the lines were also genotyped using BARCSoySNP6K assays to compare the genetic similarities between the new lines and the source genotypes. Analysis of variance results indicated that genotypic differences, year effects, and genotype × year interactions were significant for all the traits. The broad‐sense heritability of the traits was estimated to be 67.22%–98.80%, suggesting that the traits were mainly affected by genetic factors. Compared with the source materials, yield of 34 breeding lines exceeded by &gt;5%, and 17 of them had yield increases of 11.85%–41.59%. Seed weight increased significantly in 24 lines, and 11 lines showed improvements in both seed weight and yield, although there was a negative correlation between these two traits. In addition, 36 and 29 lines showed a shortened period of flowering and maturity, respectively. Plant height of 20 lines decreased by &gt;8.5 cm. Genotypic matching rate between the new lines and the source materials varied from 48.86% to 99.90%. These results demonstrated that both segregations resulting from natural crossing and intra‐cultivar/line variations could be used to improve important traits in soybean.