Cover Crops Research Articles

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.

Possibilities of biological soil management in monoculture greenhouse cultivation: cover crops, organic matter replenishment and <i>Trichoderma</i> sp. application to improve soil health

Possibilities of biological soil management in monoculture greenhouse cultivation: cover crops, organic matter replenishment and <i>Trichoderma</i> sp. application to improve soil health

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.

451 Establishing producer research sites for the development of beef and bison climate-smart agriculture

Abstract Greenhouse gas (GHG) emissions and livestock production are topics that are increasingly intertwined. While livestock production is frequently presented as harmful to the environment, this perception fails to consider the carbon sequestration that takes place in livestock grazing systems and the role that grazing ruminants have in maintaining healthy grassland ecosystems. Grazing lands, which are often unsuited to row crop agriculture, are estimated to account for 25% of the global soil sequestration potential for soil carbon storage. Grazing livestock producers are often overlooked in the GHG reduction conversation, despite over 70% of beef GHG emissions being attributed to the cow-calf sector, which is primarily grazing. Furthermore, there is a relative lack of data surrounding the implementation of climate-smart agriculture practices on grazing operations and a lack of cost-effective methods to measure carbon/GHG sinks and sources on these landscapes. The potential climate benefits are considerable, with nearly 940 million acres of rangelands in the U.S. supporting forage-based livestock production. South Dakota State University recently received funding to support a large-scale climate-smart project focused on grazing beef and bison. For this project, we are utilizing the Cottonwood Field Station that has over 80 yr of historical grazing data, along with six grazing beef and bison ranching operations in the Northern Great Plains. The work on these ranches will focus on measuring and monitoring the impacts of climate-smart NRCS land practices, including cover crops, improved pasture and range seedings, prescribed grazing, and prescribed burning. A significant focus will be extensive, annual soil sampling across these operations to continuously monitor changes in soil organic carbon, bulk density, texture, pH, and soil microbiome community profiles. Each operation will have GreenFeed methane pasture units deployed for 5 yr to monitor changes in beef cattle and bison methane emissions in response to climate-smart practice adoption. We are also assessing biodiversity pre and post climate-smart practice adoption, as biodiversity improvements may be one of the most overlooked and yet most important responses to climate-smart land practice implementation. Over the duration of this project, a large-scale, comprehensive data collection will be achieved and used to refine GHG and carbon sequestration estimates associated with range livestock systems. Data resulting from this project will be used to inform research protocols and prediction models, fill knowledge gaps, and shape science-based discussions and marketing strategies for climate-smart agricultural commodities.

Soybean performance under the biomass of Quênia guinea grass cover crops after intercropping with maize

Soil cover biomass in integrated systems can improve agronomic characteristics and impact in soybean productivity. Thus, the objective is to evaluate the desiccation efficiency, biomass production, nutrient accumulation, and soybean productivity in Quênia guinea grass (Panicum maximum cv. BRS Quênia) biomass after with an intercropped with maize in different forms of seeding, in compare the conventional soybean. The experimental design was a randomized block with four replicates. The treatments consisted of cover crop biomass: maize monoculture; Quênia guinea grass monoculture; maize intercropped with Quênia guinea grass within rows; maize intercropped with Quênia guinea grass between rows and an additional soybean treatment without cover biomass. The results showed that the Quênia guinea grass in the different cropping systems presented high desiccation efficiency, showing potential as a cover plant in integrated systems. The intercropping system of maize with Quênia guinea grass between rows is more recommended than seeding in the same rows as maize, because it promotes greater biomass production and nutrient cycling, reflecting in higher soybean productivity. The integrated production systems is a promising and efficient technology for production systems compared to the system of monoculture maize and soybeans without soil cover biomass, which ensures greater sustainability of agricultural systems.

Disentangling the effects of applying pig slurry or its digestate to winter wheat or a catch crop on dissolved C fluxes

The anaerobic digestion industry, which is still developing, generates biogas from organic waste products. A co-product of this process, digestate, is increasingly produced and can be recycled on agricultural land as an alternative to mineral fertilizers. Biogas digestate is a recent product whose chemical composition differs from that of its source material, and additional data still need to be acquired on its effects on dissolved carbon fluxes. The objectives of this study were to assess (i) the effects of applying biogas digestate on dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) fluxes with different winter crops, (ii) the dynamics of DOC and DIC concentrations during the drainage season, and (iii) the annual dynamics of DOC and DIC fluxes along the soil profile. The study examined effects of applying biogas digestate, pig slurry, or a mineral fertilizer to winter wheat and two catch crops (mustard and a multispecies crop) on DOC and DIC fluxes in the soil. Lysimeters at 40 cm (topsoil) and 90 cm (subsoil) depths were monitored from 2014 to 2023, from November to March (i.e., 9 winter drainage seasons). During the drainage season, the DOC concentration was highest with digestate, and its timing depended on development of the cover crop: from the beginning of the drainage season for mustard and the multispecies crop and around February for wheat. Applying digestate increased the topsoil DOC fluxes (mean of 35.7 ± 13.7 kg.ha−1 with digestate vs. 21.0 ± 6.7 kg.ha−1 with the other treatments), particularly under mustard. Topsoil DIC fluxes were highest with pig slurry due to higher mineralization than that with digestate (mean of 59.1 ± 22.8 kg.ha−1 with pig slurry vs. 46.2 ± 16.3 kg.ha−1 with the other treatments). In the subsoil, DOC fluxes were low (6.2 ± 4.1 kg.ha−1) and DIC fluxes were high (80.0 ± 45.7 kg.ha−1), with no difference among treatments.

Temporal analysis of water chemistry and smallmouth bass (Micropterus dolomieu) health at two sites with divergent land use in the Susquehanna River watershed, Pennsylvania, USA

Monitoring wild fish health and exposure effects in impacted rivers and streams with differing land use has become a valuable research tool. Smallmouth bass (Micropterus dolomieu) are a sensitive, indicator species that exhibit signs of immunosuppression and endocrine disruption in response to water quality changes and contaminant exposure. To determine the impact of agriculture and development on smallmouth bass health, two sites (a developed/agriculture site and a forested site) in the Susquehanna River watershed, Pennsylvania were selected where bass and water chemistry were sampled from 2015 to 2019. Smallmouth bass were sampled for histopathology to assess parasite and macrophage aggregate density in the liver and spleen, condition factor (Ktl), hepatic gene transcript abundance, hepatosomatic index (HSI), and a health assessment index (HAI). Land use at the developed/agriculture site included greater pesticide application rates and phytoestrogen crop cover and more detections and higher concentrations of pesticides, wastewater-associated contaminants, hormones, phytoestrogens, and mycotoxins than at the forested site. Additionally, at the developed/agriculture site, indicators of molecular changes, including oxidative stress, immune/inflammation, and lipid metabolism-related hepatic gene transcripts, were associated with more contaminants and land use variables. At both sites, there were multiple associations of contaminants with liver and/or spleen macrophage aggregate density, indicating that changes at the molecular level seemed to be a better indicator of exposures unique to each site. The findings illustrate the importance of timing for land management practices, the complex mixtures aquatic animals are exposed to, and the temporal changes in contaminant concentration. Agricultural practices that affect hepatic gene transcripts associated with immune function and disease resistance were demonstrated which could negatively affect smallmouth bass populations.

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

The Soil and Water Management Research Unit of the USDA-Agricultural Research Service is located in St. Paul, MN, and conducts long-term research at the University of Minnesota Research and Outreach Center located at Rosemount, MN. As part of USDA's Long-Term Agroecosystem Research (LTAR) network, the croplands common experiment (CCE) at this location is focused on integration of a kura clover (Trifolium ambiguum M. Bieb.) living mulch (KCLM) system into the prevailing 2-year rotation of corn (Zea mays L.) and soybean (Glycine max L.) that is typical of the midwestern Corn Belt. The LTAR-CCE conducted at Rosemount, MN, aims to compare the long-term environmental and agronomic performance of KCLM while identifying challenges and developing management strategies for this alternative practice. The use of a living mulch for this region is advantageous because, once established, it does not require additional time for fall field operations typically associated with winter cover crops. Results from LTAR-CCE studies at this site show that KCLM results in a substantial increase in soil field-saturated hydraulic conductivity and decreases in leaching of nitrate-nitrogen (NO3 --N). Disadvantages of the KCLM system include potential for increased emissions of nitrous oxide (N2O) and reduced crop yields, particularly during drought. Also, the optimal approach for crop row establishment in the spring remains uncertain. Ongoing LTAR-CCE research with KCLM aims to better understand and quantify both benefits and risks across conditions of interannual weather variability and changing climate to develop guidance for suitable adoption and management of this alternative practice.

Cover crop grazing length impacts on soil health and crop productivity in US Coastal Plain soils

AbstractIntroducing integrated crop‐livestock systems into row crop production promotes income diversification and potential soil health benefits through cover crop grazing on degraded soils of the southeastern United States, but effects of these practices on crop yields and soil health in Coastal Plain soils are not well established. A 4‐year study was performed to evaluate the effects of grazing a multi‐species winter cover crop on soil health and crop yields under mid‐February, mid‐March, and mid‐April cattle removal dates and a non‐grazed control within an annual cotton (Gossypium hirsutum L.) and peanut crop rotation (Arachis hypogaea L.). Chemical soil health indicators (soil organic carbon and permanganate oxidizable carbon), physical soil health indicators (water‐stable aggregates and penetration resistance), biological soil health indicators (microbial biomass carbon, soil respiration, and arbuscular mycorrhizal fungi colonization), crop yield, and cover crop biomass were evaluated. Cover crop biomass at termination was reduced by 3660, 5250, and 5610 kg ha−1 for the mid‐February, mid‐March, and mid‐April cattle removal treatments compared to the non‐grazed control. No grazing treatment effects were observed for biological soil properties. Soil organic carbon was higher in the non‐grazed treatment than the mid‐April grazing treatment across 0‐ to 30‐cm depth. Penetration resistance across 0‐ to 50‐cm depth and water‐stable aggregates at the 0‐ to 30‐cm depth were both negatively impacted by increased grazing period lengths. Results from this study suggest that longer cover crop grazing periods have little effect on biological and chemical soil health indicators in the short term but can negatively impact some physical soil health indicators.