Cover Crops Research Articles

Cover crop root exudates impact soil microbiome functional trajectories in agricultural soils

BackgroundCover cropping is an agricultural practice that uses secondary crops to support the growth of primary crops through various mechanisms including erosion control, weed suppression, nutrient management, and enhanced biodiversity. Cover crops may elicit some of these ecosystem services through chemical interactions with the soil microbiome via root exudation, or the release of plant metabolites from roots. Phytohormones are one metabolite type exuded by plants that activate the rhizosphere microbiome, yet managing this chemical interaction remains an untapped mechanism for optimizing plant-soil-microbiome interactions. Currently, there is limited understanding on the diversity of cover crop phytohormone root exudation patterns and our aim was to understand how phytochemical signals selectively enrich specific microbial taxa and functionalities in agricultural soils.ResultsHere, we link variability in cover crop root exudate composition to changes in soil microbiome functionality. Exudate chemical profiles from 4 cover crop species (Sorghum bicolor, Vicia villosa, Brassica napus, and Secale cereal) were used as the chemical inputs to decipher microbial responses. These distinct exudate profiles, along with a no exudate control, were amended to agricultural soil microcosms with microbial responses tracked over time using metabolomes and genome-resolved metatranscriptomes. Our findings illustrated microbial metabolic patterns were unique in response to cover crop exudate inputs over time, particularly by sorghum and cereal rye amended microcosms. In these microcosms, we identify novel microbial members (at the genera and family level) who produced IAA and GA4 over time. Additionally, we identified cover crop exudates exclusively enriched for bacterial nitrite oxidizers, while control microcosms were discriminated for nitrogen transport, mineralization, and assimilation, highlighting distinct changes in microbial nitrogen cycling in response to chemical inputs.ConclusionsWe highlight that root exudate amendments alter microbial community function (i.e., N cycling) and microbial phytohormone metabolisms, particularly in response to root exudates isolated from cereal rye and sorghum plants. Additionally, we constructed a soil microbial genomic catalog of microorganisms responding to commonly used cover crops, a public resource for agriculturally relevant microbes. Many of our exudate-stimulated microorganisms are representatives from poorly characterized or novel taxa, revealing the yet to be discovered metabolic reservoir harbored in agricultural soils. Our findings emphasize the tractability of high-resolution multi-omics approaches to investigate processes relevant for agricultural soils, opening the possibility of targeting specific soil biogeochemical outcomes through biological precision agricultural practices that use cover crops and the microbiome as levers for enhanced crop production.BPG5Z_iDvqR4KrAs2TcSTiVideo

Medium-term economic impacts of cover crop adoption in Maryland

Cover cropping has the potential to generate private economic benefits to farm operations as well as larger-scale environmental benefits to the broader community. However, cover crop adoption remains limited in the United States (US) (i.e., 4.7 % in 2022 (USDA-NASS, 2024)), primarily due to uncertainty in economic outcomes, with several studies showing potentially negative net returns from cover crop use in the short-term (1–3 years). This study investigates the medium-term (5–7 years) economic impact of cover crop adoption using plot-level data from field experiments in the state of Maryland. The empirical analysis employs ordinary least squares (OLS) statistical models and partial budgeting techniques to achieve the study objective. Our results show that cover crops do not have a statistically significant effect on crop yield, fertilizer costs, or pesticide costs, but we find that cover crop use statistically increases field operation and seed expenses. As a result, the private net return from cover crop adoption in the medium term is generally negative based on the Maryland field trial data used in the analysis. Specifically, the average net return per acre with cover crops is approximately $60-$90 lower for corn and around $60 lower for soybeans compared to fields without cover crops. This empirical finding suggests that public support through incentive payments may help further incentivize cover crop adoption in the US, which can then provide environmental and ecosystem services that are of benefit to the general public.

Optimizing crop seeding rates on organic grain farms using on farm precision experimentation

Organic agriculture is often regarded as less damaging to the environment than conventional agriculture, though at the expense of lower yields. Field-specific precision agriculture may benefit organic production practices given the inherent need of organic farmers to understand spatiotemporal variation on large-scale fields. Here the primary research question is whether on-farm precision experimentation (OFPE) can be used as an adaptive management methodology to efficiently maximize farmer net returns using variable cover crop and cash crop seeding rates. Inputs of cash crop seed and previous-year green manure cover crop seed were experimentally varied on five different farms across the Northern Great Plains from 2019 to 2022. Experiments provided data to model the crop yield response, and subsequently net return, in response to input (seeding) rates plus a suite of other spatially explicit data from satellite sources. New, field-specific spatially explicit optimum input rates were generated to maximize net return including temporal variation in economic variables. Inputs were spatially optimized and using simulations it was found that the optimization strategies consistently out-performed other strategies by reducing inputs and increasing yields, particularly for non-tillering crops. By adopting site specific management, the average increase in net return for all fields was $50 ha−1. These results showed that precision agriculture technologies and remote sensing can be utilized to provide organic farmers powerful adaptive management tools with a focus on within-field spatial variability in response to primary input drivers of economic return. Continued OFPE for seeding rate optimization will allow quantification of temporal variability and subsequent probabilistic recommendations.

Within‐field variation of crop yield loss from cover crops

Within‐field variation of crop yield loss from cover crops

Cover cropping attenuates population growth of Macrophomina phaseolina by limiting weed biomass, despite asymptomatic colonization of cover crops.

Macrophomina phaseolina is a fungus that causes charcoal rot in strawberry and a wide variety of crop species. Little is known about its potential to asymptomatically colonize crop plants or grow saprophytically on their tissues, both of which would create a potential for alternate, asymptomatic hosts to lead to increases in inoculum. To test the impact of cover cropping on M. phaseolina abundance, we conducted randomized-block field experiments in soils infested by M. phaseolina. None of the fifteen cover crop varieties showed symptoms of charcoal rot. All Fabaceae and Brassicaceae varieties were asymptomatically colonized at varying rates, but among Poaceae, M. phaseolina was recovered from only one individual oat plant. Soil samples collected at the time of planting, tillage, and 8 weeks post-tillage showed that cover cropping attenuated the growth of M. phaseolina relative to fallow plots harboring the weedy legume Medicago polymorpha. This weed species was abundantly colonized by this pathogen in both living root samples and plant residue collected 8 weeks after tillage. Cover cropping also influenced the diversity and composition of bulk soil bacterial and fungal communities, but these effects were not associated with M. phaseolina population density. Although M. phaseolina was not detected in living wheat tissues, it was recovered from wheat residue, suggesting that it may be facultatively saprophytic. These results suggest that cover cropping does not pose a risk for increasing disease caused by M. phaseolina and could be beneficial as conducive weed species, such as M. polymorpha, are suppressed.

Enhancing Agroecology in Pepper (Piper nigrum L.) Cultivation with Centrosema pubescens Ground Cover: A Study from Central Bangka, Indonesia

<p>Pepper cultivation in Bangka Island primarily takes place on sandy land. Legume cover crops, such as <em>Centrosema pubescens</em>, which has been widely used as a ground cover, are anticipated to improve land quality by maintaining soil temperature and humidity, increasing soil organic carbon content, increasing soil porosity, and improving soil fertility. This research aims to analyze the agroecosystem of pepper plants, by comparing the use of the cover plant <em>C. pubescens</em> as a soil treatment and the absence of using these cover plants as a control. The research was conducted in farmers’ pepper gardens in Perlang Village, Central Bangka Regency, Bangka Belitung Province. The research employed a randomized block design, with <em>C. pubescens</em> and natural vegetation as treatments, each replicated 3 times. The variables measured were soil temperature and humidity; abundance of microorganisms; weed density, frequency, and dominance; chlorophyll a, chlorophyll b, and anthocyanin content of pepper leaves; photosynthesis rate; transpiration rate; stomatal conductance; soil chemical and physical properties. The results showed that <em>C. pubescens</em> as ground cover could reduce the dominance of the <em>Bidens pilosa</em> weed (relative dominance of 36.16%) but led to an increase in the dominance of <em>Chromolaena odorata</em> (relative dominance of 38.7%). <em>C</em>.<em> pubescens</em> ground cover could also maintain stable soil temperature and moisture, and increase P, K, Ca, and Mg soil content by 100%, 100%, 43.6%, and 48.3%, respectively. Furthermore, pepper plants grown with <em>C. pubescens</em> exhibited 25%, 23.7%, and 16% higher chlorophyll a, total chlorophyll, and carotenoid content, respectively, compared to those grown without the cover crop.</p>

Genetic Variation in the Benefits Cover Crops Provide Cash Crops

Genetic Variation in the Benefits Cover Crops Provide Cash Crops

Integrated Management of Sclerotinia Stem Rot of Soybean Including Organically-Allowed Fungicides in the Midwest United States

Sclerotinia stem rot (SSR) is an economically important disease of soybean, especially in the Great Lakes region of the United States. Few studies on SSR have considered organic management practices and how integrated management techniques best apply in these systems. Trials in Wisconsin and Indiana aimed to evaluate management techniques available to organic farmers for their efficacy in SSR control while preserving yield. The practices evaluated included genetically resistant cultivars, tillage techniques, and the application of Organic Materials Review Institute (OMRI) listed foliar fungicides. The resistant cultivar had significantly lower SSR than the susceptible cultivar in three site-years (P < 0.01). The resistant cultivar also had significantly greater yield than the susceptible cultivar in two site-years (P < 0.05). Using a roller-crimped rye cover crop resulted in mixed results on SSR and yield. No effect on SSR or yield by OMRI listed foliar fungicides was observed (P > 0.05). Overall, the results emphasize the importance of planting a resistant soybean cultivar to manage SSR in organic farming systems in the Midwestern United States.

The LTAR Cropland Common Experiment at Upper Mississippi River Basin-Morris.

The Upper Mississippi River Basin (UMRB) Long-Term Agroecosystem Research (LTAR) watershed is hydrologically complex, with a notable temperature and precipitation gradient across four locations: Ames, IA; Platteville, WI; Morris, MN; and St. Paul, MN. Each location established LTAR Croplands Common Experiment (CCE) scenarios to fit local climatic and cultural practices. This paper describes the UMRB-Morris location, which was established in 2016 and is the most northern of the sites and contributes to the major watersheds of the UMRB and the Red River of the North. Both on-farm and plot-scale studies are included. The prevailing system is a corn (Zea mays L.)-soybean (Glycine max L.) rotation with annual deep ripping tillage. The signature alternative system is alternative 1, which is a shallow strip-till in a corn-soybean rotation. A second alternative system includes shallow tillage/rotational no-tillage in a corn-soybean-wheat (Triticum aestivum L.) with winter oilseed and cover crops, and it is considered a test ground for future alternative systems. On-farm fields are equipped with eddy covariance towers and include 16 geo-referenced soil core sampling sites for incremental samplings. Each field is sampled annually for crop yield and management data are recorded. Plot-scale versions of the treatments are managed at the Swan Lake Research Farm. On-farm and plot-scale fields are instrumented with Phenocams to capture continuous photographic records. The CCE at UMRB-Morris aims to integrate soil, crop, weather data, and image classification to assess benefits and challenges across different management strategies.

Awareness and adoption of climate-resilient practices by smallholder farmers in central and upper Eastern Kenya

Climate change and soil fertility decline are major hurdles to agricultural ecosystems. Despite the importance of climate-resilient practices (CRPs) in enhancing food security, poverty alleviation and nutritional security, awareness and adoption remain low in most developing countries, including Kenya. We assessed the determinants of simultaneous awareness and adoption of CRPs and their intensity in Central Highlands of Kenya. The CRPs considered in this study were inorganic fertilizer and manure integration, manure, mulching, crop residues, cover crop, crop rotation and intercrop. The study used a cross-sectional survey design and collected data from 400 smallholders in Central Highlands of Kenya. The data were analyzed using descriptive statistics, multivariate probit and Poisson regression. Our findings showed that awareness and adoption of specific CRPs and their intensity were determined by occupation, age, farming experience, household size, soil fertility management, climate change adaptation, agricultural training, and geographical location. Smallholders’ agricultural training was an important determinant of awareness, adoption level, and intensity. Our findings underscored the need for agricultural policymakers and extension systems to design farmer-driven training programs for enhanced awareness and adoption of CRPs.

Genetic and environmental drivers of legume cover crop performance: Hairy vetch

AbstractAmong 50 environments in the United States, we screened 35 hairy vetch (Vicia villosa Roth.) lines for traits of interest to cover cropping. We analyzed the influence of genotype, environment, and the genotype‐by‐environment interaction (G × E) on biomass, vigor, winter survival, emergence, flowering time, and nitrogen fixation. To explore how environments and G × E impacted each trait, we associated environment predictions and G × E loadings with weather and soil parameters. Environment had the largest influence on all traits, representing more than half of the variance. Environment predictions were significantly associated with weather and/or soil parameters for each trait. Biomass was associated with growing degree days, winter survival with freezing degree days without snow cover, growth stage with shortwave radiation, and emergence with soil texture. The G × E interaction was larger than genotypic variance for all traits except for winter survival and flowering time. The G × E interaction loadings were associated with soil sand content for biomass, air temperature for fall vigor and emergence, and snow cover for winter survival. Although it represented the smallest proportion of total variance, genetic effects were significant for all traits except for emergence, Ndfa, %N, and C:N. New hairy vetch breeding lines were superior to all commercially available lines for biomass and winter survival. Biomass harvest timing did not significantly change line rank, indicating that top‐performing lines can be used in diverse management systems. To select for high nitrogen contribution to subsequent crops, breeding programs can indirectly select for biomass rather than expensively evaluating symbiotic nitrogen fixation.

RGB‐based indices for estimating cover crop biomass, nitrogen content, and carbon:nitrogen ratio

AbstractPlant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low‐cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high‐throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red‐green‐blue (RGB) indices collected with standard settings in non‐standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R2 = 0.554, root mean square error [RMSE] = 219.29 kg ha−1), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R2 = 0.573, RMSE = 3.5 g kg−1) and carbon:nitrogen ratio (R2 = 0.574, RMSE = 1.29 g g−1). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.

Scientometric analysis of cover crop management: Trends, networks, and future directions

This research paper presents a comprehensive scientometric analysis of English articles from the Scopus database regarding the topic of cover crop management from 1956 to March 2024. Through the analysis of the annual production trend, total production, a co-occurrence network of keywords, co-authorship networks, and co-citation networks, the data was mapped and visualized using VOSviewer and Bibliometrix software. There was an exponential increase in publications from 1991 onwards. The predominant subject was Agricultural and Biological Sciences and the most relevant journals, authors, and documents were related to this topic. Additionally, the most productive country was the United States, but in terms of article production per surface area, other countries, such as Switzerland and The Netherlands, evidence the great weight of cover crop management in these countries. The identified research topics were related to the application of different crops as cover crops and the effects on different cultivars, also soil quality improvement, and fertilization and nutrient efficiency. An emerging research topic was found to be the usefulness of cover crops as effective tools for climate change adaptation and mitigation strategies in agriculture. Future research in the field of cover crop management could be directed towards climate-adaptive cover crop species and varieties, the use of technological innovations for cover crop monitoring, and management and analysis of the economic and social impacts of cover crop adoption.

Influence of crop rotation, the treatment of crop residues, and the application of nitrogen fertilizers on soil properties and maize yield

AbstractThis inquiry is aimed at discerning the impact of various agricultural practices, such as crop rotation, the incorporation of plant residues, and the application of mineral fertilizers, on soil health and crop productivity, notably focusing on maize production. Cultivation included maize (Zea mays), velvet beans (Mucuna pruriens), soybeans (Glycine max), and vetch (Vicia sativa). After harvest, maize seeds were sown across all 48 plots to evaluate the influence of preceding crop rotation on soil properties and maize yield. Hypotheses posited in the study suggested that crop rotation, nitrogen fertilizer application, and the incorporation of crop residues positively impact soil fertility. The study further argues that the utilization of cover crops in crop rotation aids in nitrogen retention within the soil and enhances yield. The results were processed utilizing a two‐way analysis of variance (ANOVA) with interaction and post hoc comparisons. The findings confirm that crop rotation, nitrogen fertilizer application, and incorporation of crop residues influence soil fertility. The study found that crop rotation and nitrogen fertilizers have a significant impact on soil properties. Crop rotations such as “velvet beans‐maize” and “soybeans‐maize” increased soil fertility by 10%–15% compared to crop rotations of “vetch‐maize” and maize monocultures. Nitrogen fertilizers increased the total nitrogen content in the soil by 5%–10% in both years. Crop residues also positively affected soil properties, increasing pH and total nitrogen by 1%–5%. The study demonstrates that crop rotation, nitrogen fertilizers, and crop residues can be effective management methods for improving soil fertility and reducing the risk of nitrate leaching.

No-tillage farming enhances widespread nitrate leaching in the US Midwest

Abstract Conservation tillage has been promoted as an effective practice to preserve soil health and enhance agroecosystem services. Changes in tillage intensity have a profound impact on soil nitrogen cycling, yet their influence on nitrate losses at large spatiotemporal scales remains uncertain. This study examined the effects of tillage intensity on soil nitrate losses in the US Midwest from 1979–2018 using field data synthesis and process-based agroecosystem modeling approaches. Our results revealed that no-tillage (NT) or reduced tillage intensity (RTI) decreased nitrate runoff but increased nitrate leaching compared to conventional tillage. These trade-offs were largely caused by altered water fluxes, which elevated total nitrate losses. The structural equation model suggested that precipitation had more pronounced effects on nitrate leaching and runoff than soil properties (i.e. texture, pH, and bulk density). Reduction in nitrate runoff under NT or RTI was negatively correlated with precipitation, and the increased nitrate leaching was positively associated with soil bulk density. We further explored the combined effects of NT or RTI and winter cover crops and found that incorporating winter cover crops into NT systems effectively reduced nitrate runoff but did not significantly affect nitrate leaching. Our findings underscore the precautions of implementing NT or RTI to promote sustainable agriculture under changing climate conditions. This study provides valuable insights into the complex relationship between tillage intensity and nitrate loss pathways, contributing to informed decision-making in climate-smart agriculture.

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

Multi-year soil response to conservation management in the Virginia Coastal Plain

In the coastal plain region of the United States, conservation agriculture practices are being implemented to improve soil health, minimize environmental impacts, and improve farm profitability. Common practices include cover cropping and conservation tillage using strip tillage, minimal tillage, or no tillage. However, the soil response to specific combinations of conservation tillage and cover crop rotations remains poorly quantified. The objective of this research was to evaluate changes in soil properties from different combinations of conservation management. Four tillage systems – conventional, strip, minimal, and no tillage – and three winter cover rotations – fallow, winter cash crop, and high-biomass cover crop – were tested in a split-plot design. Bulk density, depth to a root-restrictive layer, soil carbon concentration, soil carbon stock, field-saturated hydraulic conductivity, and yield were measured over a seven-year period. Bulk density and field-saturated hydraulic conductivity showed greater temporal variation in the strip tillage and conventional tillage practices. Depth to root-restrictive layer was consistently highest in the strip and minimal tillage treatments, which both included implements designed to alleviate subsoil compaction. Treatments that combined conservation tillage with a winter cover (i.e., cash crops or high-biomass cover crops) had greater increases in soil carbon concentrations and carbon stock. Summer cash crop yield was significantly increased following the high-biomass cover crop treatment in 2 out of the 7 years. Altogether, soil carbon showed a more consistent response to conservation management than the other soil properties, which tended to show greater variability based on the time since disturbance (e.g., tillage). Conservation management practices therefore need to be consistently applied for multiple years in order to improve soil properties such as bulk density and saturated hydraulic conductivity.

Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques

Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R2 = 0.89, RMSE = 0.04 cm3cm−3). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm3cm−3. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.

Residues of Symbiont Cover Crops Improving Corn Growth and Soil-Dependent Health Parameters

Cover crops have emerged as a crucial tool in promoting sustainable agricultural practices, particularly in improving soil quality and soil–plant health. This study investigates the impact of single cover crop plants each with varying fungal and/or bacterial symbiosis capacities in a pot experiment. The growth of non-symbiont Ethiopian mustard (Brassica carinata), the associative bacterium symbiont black oat (Avena strigosa) and the double (fungus–bacterium) endosymbiont broad bean (Vicia faba) was studied on three distinct soil types, namely a less-fertile sandy soil (Arenosol), an average value of loam soil (Luvisol) and a more productive chernozem soil (Chernozem). Beside the biomass production, nitrogen content and frequency of AM fungi symbiosis (MYCO%) of cover crops, the main soil health characteristics of electrical conductivity (EC), labile carbon (POXC) and fluorescein diacetate enzyme activity (FDA) were assessed and evaluated by detailed statistical analysis. Among the used soil types, the greatest biomass production was found on Chernozem soil with the relatively highest soil organic matter (2.81%) content and productivity. Double symbiotic activity, assessed by soil nitrogen content and mycorrhiza frequency (MYCO%), were significantly improved on the lowest-quality Arenosols (SOM 1.16%). In that slightly humous sandy soil, MYCO% was enhanced by 45%, indicating that symbiosis was crucial for plant growth in the less-fertile soil investigated. After the initial cover crop phase, the accumulated biomass was incorporated into the Luvisol (SOM 1.64%) soil, followed by the cultivation of corn (Zea mays, DK 3972) as the main crop. The results indicate that incorporating cover crop residues enhanced labile carbon (POXC) by 20% and significantly increased the FDA microbial activity in the soil, which positively correlated with the nutrient availability and growth of the maize crop. This study emphasizes the importance of selecting suitable cover crops based on their symbiotic characteristics to improve soil quality and enhance soil–plant health in sustainable agricultural systems.

394 Cover crops for efficient grazing systems

Abstract Grazing of cool-season cover crops provides a sustainable solution for extending the grazing season while potentially reducing the environmental impact of conventional row-crop farming practices. The primary objective of this study was to evaluate how different mixtures of cover crops affect stocker performance when grazed. The study was conducted across 18 paddocks that were sown with grass [‘Coker’ oat (Avena sativa)], grass + legume [‘Dixie’ crimson clover (Trifolium incarnatum)], or grass + legume + brassica [‘Turnip’ brassica (Brassica rapa subsp.; rapa)] at the Wiregrass Research and Extension Center in Headland, AL. These paddocks were randomly assigned to either grazed or un-grazed conditions (n = 3). In this study, yearling beef steers with an average body weight (BW) of 266 ± 44 kg were grazed in paddocks using the put and take method to maintain a forage allowance of 1 kg dry matter per kilogram of BW. Forage samples were collected biweekly to analyze biomass, botanical composition, and forage nutritive value. Grazing occurred from January 17 to March 22, 2023 (65 d). Steer BW were recorded at the beginning and end of the grazing season to calculate average daily gain. The data were subjected to analysis as a two-way ANOVA using Glimmix in SAS 9.4 (SAS Institute, Cary, NC), harvest date was considered a repeated measure. No treatment × grazing interaction was observed on forage yield performance [average (AVG) forage yield kg DM/ha; P = 0.460]. In addition, no difference was observed among the different cover crop mixtures (AVG forage yield kg DM/ha; P = 0.670). Grazed paddocks had a greater crude protein when compared with ungrazed paddocks (Grazed vs. Ungrazed %; P = 0. 017). However, there was no effect of cover crop mixture on crude protein (CP) concentration (AVG CP %; P = 0.670 for treatment effect). These results indicate that cool-season annual cover crops can provide sufficient forage yield and quality to support sufficient animal gains, even during the relatively short grazing season. Incorporating cover crops not only improves agricultural sustainability but can also enhance the resilience and long-term health of the agroecosystem.