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The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.

Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha–1, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r2 = 0.813) through levels of 9,000 kg ha–1. Based on independent data validation, predicted biomass and measured biomass were linearly related (r2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.

Soils represent the largest terrestrial sink of carbon (C) on Earth, yet the quantification of the amount of soil organic carbon (SOC) is challenging due to the spatial variability inherent in agricultural soils. Our objective was to use a grid sampling approach to assess the magnitude of SOC variability and determine the current SOC stocks in three typical agricultural fields in Maryland, United States. A selected area in each field (4000 m2) was divided into eight grids (20 m × 25 m) for soil sample collection at three fixed depth intervals (0–20 cm, 20–40 cm, and 40–60 cm). Soil pH in all fields was significantly (p < 0.05) greater in the surface soil layer (6.2–6.4) than lower soil layers (4.7–5.9). The mean SOC stocks in the surface layers (0–20 cm: 1.7–2.5 kg/m2) were 47% to 53% of the total SOC stocks at 0–60 cm depth, and were significantly greater than sub-surface layers (20–40 cm: 0.9–1.3 kg/m2; 40–60 cm: 0.8–0.9 kg/m2). Carbon to nitrogen (C/N) ratio and stable C isotopic composition (δ13C) were used to understand the characteristics of SOC in three fields. The C/N ratio was positively corelated (r > 0.96) with SOC stocks, which were lower in sub-surface than surface layers. Differences in C/N ratios and δ13C signatures were observed among the three fields. The calculated values of SOC stocks at 0–60 cm depth ranged from 37 to 47 Mg/ha and were not significantly different in three fields likely due to the similar parent material, soil types, climate, and a short history of changes in management practices. A small variability (~10% coefficient of variation) in SOC stocks across eight sampling grids in each field suggests that re-sampling these grids in the future can lead to accurately determining and tracking changes in SOC stocks.

Typically, weed density is used to predict weed-induced yield loss, as it is easy and quick to quantify, even though it does not account for weed size and time of emergence relative to the crop. Weed–crop leaf area relations, while more difficult to measure, inherently account for differences in plant size, representing weed–crop interference more accurately than weed density alone. Unmanned aerial systems (UASs) may allow for efficient quantification of weed and crop leaf cover over a large scale. It was hypothesized that UAS imagery could be used to predict maize (Zea mays L.) yield loss based on weed–crop leaf cover ratios. A yield loss model for maize was evaluated for accuracy using 15- and 30-m-altitude aerial red–green–blue and four-band multispectral imagery collected at four North Carolina locations. The model consistently over- and underpredicted yield loss when observed yield loss was less than and greater than 3,000 kg ha−1, respectively. Altitude and sensor type did not influence the accuracy of the prediction. A correction for the differences between predicted and observed yield loss was incorporated into the linear model to improve overall precision. The correction resulted in r2 increasing from 0.17 to 0.97 and a reduction in root mean-square error from 705 kg ha−1 to 219 kg ha−1. The results indicated that UAS images can be used to develop predictive models for weed-induced yield loss before canopy closure, making it possible for growers to plan production and financial decisions before the end of the growing season.

\"We can live with the Palestinians, but not with the PLO or [Arafat]... The PLO terrorists under his leadership murdered schoolchildren in Avivim, Ma'alot and Antwerp, as well as the Olympic athletes from Israel in Munich, and Jewish worshipers in an Istanbul synagogue. Arafat's terrorists also killed a child and his pregnant mother in Alfei Menashe, and a mother and her children on a bus going to Jericho. Sir, - It is with sadness and frustration that I read Yosef Goell's self-deluding piece on the tragedy of America's disappearing Jews (\"When Pattie met Pete,\" September 15) in which he laments the assimilation and intermarriage decimating American Jewry. One of the problems, in his mind, is the failure to instill knowledge and pride in recent Jewish achievements rather than in the ancient or \"mumbo-jumbo\" aspects of religious ritual. It is scandalous for an intellectual to discuss this issue and utterly omit the lessons learned from the enormous success of Orthodoxy, modern or haredi, in raising committed Jews; this omission raises serious questions about the author's bias and intellectual integrity. To dismiss the \"mumbo-jumbo\" of ritual - when the glaring lack of ritual among secular Jews is exactly why they are disappearing - is bizarre.

[...]in a 2-1 decision that featured a forceful dissent by Judge Amalya L. Kearse, the Second Circuit adopted an expansive definition of \"property\" for purposes of the wire fraud and Title 18 securities fraud statutes, holding that \"predecisional\" confidential government information relating to planned medical treatment reimbursement rate changes constituted government \"property\" necessary to bring insider trading cases under an embezzlement or misappropriation theory. [...]absent a breach by the insider, there is no derivative breach\" by those who passed on or traded on the inside information. Because the insiders in Dirks did not benefit from their disclosures to Dirks, but rather \"were motivated by a desire to expose the fraud,\" they did not breach any fiduciary duty; Dirks thus \"had no duty to abstain from use of the inside information that he obtained.\" 3 What constitutes a \"personal benefit\" under Dirks has been the subject of significant litigation in recent years, including the extent to which gifting of confidential information to friends or relatives satisfied the personal benefit test.4 In the 2002 Sarbanes-Oxley Act, Congress added a new securities fraud provision to the criminal code, 18 U.S.C. § 1348, to \"supplement the patchwork of existing technical securities law violations with a more general and less technical provision, with elements and intent requirements comparable to current bank fraud and health care fraud statutes. Distinguishing Carpenter, the Supreme Court noted that \"whatever interests Louisiana might be said to have in its video poker licenses, the State's core concern is regulatory\" and those interests \"cannot be economic.\" (emphasis in original). Because \"the State did not decide to venture into the video poker business,\" but instead \"permit[ted], regulate[d], and tax[ed] private operators of the games,\" the Court concluded that the licenses in the State's hands did not constitute \"property.\"