Natural Resources Conservation Service

Quantitative PCR (qPCR) is a common, culture-independent method to estimate gene copies at levels as broad as Kingdom and specific as guild, function or species; however, it is dependent upon several factors that affect the amplification efficiency. Amplification efficiency of an individual qPCR reaction is dependent on template type, qPCR product, and assay optimization including primer design. Although qPCR reactions can be optimized, the design of primers to meet guidelines for relatively short amplicon length (75–150 base pairs) is often unfeasible especially when targeting a broad phylogeny of organisms or functional genes. The addition of exogenous non-target plasmid DNA (0.05 ng µL-1) as a PCR additive significantly increases the amplification efficiency for long target amplicons up to 723 bp. The method is applicable for different template types including circular and linearized plasmids (24–30% increase) and cell-free DNA extracts (up to 26%), as well as different thermocyclers and qPCR products. As an exogenous DNA source, the pUC19 plasmid provides flexibility with a reduced chance of non-target amplification due to the relatively simple, short, and known DNA sequence. Overall, this method enhances amplification efficiency, reduces template bias, and lowers qPCR variability with a strong benefit for long amplicon assays.

ABSTRACTTemporary baiting is often used to increase detection rates during camera surveys, particularly for white‐tailed deer (Odocoileus virginianus). However, the effects of bait on deer space use and movement have not been examined using location data sampled at intervals matching typical bait‐site visits. We defined three bait periods (pre‐bait, bait, post‐bait) to evaluate the effects of presence and removal of bait (i.e., shelled corn) on deer space use and movement relative to control deer not exposed to bait. We captured and fitted 61 deer (32 F, 29 M) in central Texas with GPS collars, that attempted a GPS location every 15 min from July 22 to September 2, 2012–2013. Of these (3 recaptures), 41 (20 F, 21 M) and 23 (15 F, 8 M) collars were assigned to the impact and control groups, respectively. We used generalized linear mixed‐effects models to evaluate the effect of bait on daily estimates of range size (ha), range overlap (proportion, 0–1), distance of activity centers to the nearest bait site (m), and distance traveled (m/15‐min) by deer. Deer exposed to bait expanded ranges and moved more during the active period (crepuscular, night) but moved less during the inactive period (day) relative to the pre‐bait period, indicating complex, time‐dependent responses. Deer exposed to bait did not shift their activity centers toward bait sites when bait was present. After bait removal, deer continued foraging within previously established ranges rather than abandoning the area. These results suggest that temporary baiting is unlikely to attract deer from outside their normal ranges, and bait removal is unlikely to cause deer to leave established areas, preserving the closure assumption. Our findings have implications for wildlife managers concerned about post‐bait movements and improve understanding of time‐dependent behavioral responses to supplemental food resources.

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75 × 75 km tiles covering the 17 western states of the United States.

Abstract It is essential to evaluate the genetic gain of lint yield in modern cotton ( Gossypium hirsutum L.) cultivars planted in recent history and identify the trend of potential changes due to changes in breeding priorities in the United States. In National Cotton Variety Tests (NCVT) conducted since the 1960s, Upland cotton cultivars were tested annually at locations across the US Cotton Belt. The NCVT data from 1998 to 2022 after commercialization and inclusion of transgenic cotton cultivars were used to analyze lint yield trends during this period. The annual yield means were adjusted based on overlapped entries between testing years to minimize environmental influence during the long‐term trials for genetic gain, which was estimated from regression of the adjusted annual means over testing years. The results showed that genetic gain of lint yield was 24.1 kg ha −1 year −1 during the 25‐year period. When the long period was split into two segments, that is, 1998 to 2014 and 2015 to 2022, the genetic gains were 24.7 kg ha −1 year −1 and −1.3 kg ha −1 year −1 , respectively. The yield trend of increasing before 2015 and plateauing after 2015 coincides with the trend of stacking technology advancement in development of transgenic cultivars. This coincidence reflects the early success of stacking technologies by seed companies in pyramiding stacked genes with cotton yield during the 2000s and the middle of 2010s. The yield plateau suggests the necessity of breakthroughs in breeding methods and biotechnologies in development of transgenic cotton for further increasing yield.

The present study was carried out during winter (rabi) season of 2021–22 and 2022–23 at Krishi Vigyan Kendra, Churachandpur, (ICAR-Research Complex for NEH Region, Manipur), Manipur, to assess the yield, soil fertility, and economics of 'Arka Samrat' tomato (Solanum lycopersicum L.) variety compared to farmers' practices at six locations. The implementation of SSNM significantly enhanced soil fertility parameters, with marked improvements in soil organic carbon, SOC stock, N, P and K levels, whereas soil bulk density and pH remained unaltered. Agronomic performance was satisfactory, with yields averaging 20 t/ha across demonstration sites. Economic analysis revealed financial viability, with a mean gross return of ₹303,000/ha against a production cost of ₹97,800/ha, yielding an average net return of ₹210,500/ha. The benefit-cost ratio of 3.09 and return on investment of 2.15 accentuate the economic efficacy of the intervention. Additional economic indicators productivity per unit area (1,330), break-even point (8,150), marketable surplus (3,261), and marginal rate of return (222) further validate the financial reliability of the approach. This investigation establishes that SSNM-based tomato cultivation represents an agronomically viable and economically profitable enterprise for the sub-tropical hills of Manipur, offering a sustainable pathway to enhanced soil fertility, optimized yields, and improved agricultural livelihoods in this distinctive agroecological zone.

Abstract Context Grassland conservation efforts often prioritize intact grasslands (i.e., untilled) with native vegetation due to their ecological importance. Identifying these lands across broad geographies is crucial for conservation planning, but classifying native vegetation using remote sensing data is analytically challenging. A practical alternative is identifying undisturbed grasslands (i.e. untilled) regardless of current floristic composition, as these areas offer greater potential for native species, biodiversity, and valuable ecosystem services. Objective Identify potentially undisturbed grasslands across the contiguous US (~ 2021). Methods We used GIS processing to identify potentially undisturbed lands, and then applied supervised land cover classification to identify potentially undisturbed grassland within this boundary. This was accomplished using multiple datasets, including USDA data with high accuracy, for identifying lands with cropping history, potentially dating back to ~ 1950s. Class labels used to train the random forest model included potentially undisturbed grass, previously cultivated grass, and other cover classes, which we related to predictor variables derived from topographic, edaphic, climatic, and Sentinel-2 remote sensing datasets. Results The models performed well (mean kappa: 0.88), but performance varied across ecoregions (kappa range: [0.81–0.95]) and land cover classes (class mean F1 range: [0.83–0.97]). We estimated ~ 1.2 million km2 of potentially undisturbed grass, primarily in the western Great Plains ecoregions. Conclusion These data can support conservation planning for grassland ecosystems and serve as a baseline for monitoring future loss of potentially undisturbed grasslands. Our methods could be extended globally with other time-series land cover datasets.