Precipitation and Not Cover Crop Composition Influenced Corn Economic Optimal N Rate and Yield

The effects of single species cover crops on corn (Zea mays L.) N requirement and grain yield are well studied throughout the U.S. Midwest. However, comparing cover crop mixes that include different compositions of grass and broadleaf species is limited. Fourteen corn N response experiments were conducted in South Dakota from 2018 to 2021. Fall cover crops planted after small grain harvest were mixtures of dominantly grasses, broadleaves, a 50/50 grass/broadleaf mixture, and a no cover crop control. Compared to the control, including a cover crop led to no differences in economic optimal N rate (EONR) and yield at zero N (0N) and yield at EONR 44%, 62%, and 83% of the time, respectively. As spring cover crop/residue biomass and its C and N content increased, corn yield at EONR decreased and EONR increased when including cover crops (R2 = 0.36–0.56). Including cover crops reduced EONR and resulted in a similar yield when precipitation increased above 850 mm. When differences occurred with economic return from N, including a cover crop reduced economic return in 3 site-years (mean decrease of US$358 ha−1) and in only 1 site-year did including a grass cover crop increase economic return from N (+US$335 ha−1). Thus, in the first year of growing cover crops (i.e., grasses, broadleaves, or a grass/broadleaf mix) before corn, growers can normally expect some differences in EONR. However, with the appropriate rate of N, yield at EONR is maintained and any economic differences from N normally minimized

Cover Crop Composition in Long-term No-till Soils in Semi-arid Environments Do Not Influence Soil Health Measurements after One Year

Evaluating the influence of grass or broadleaf cover crops on soil health measurements is common in the northern US Midwest. However, the comparison among different cover crop mixtures, including blends of both grass and broadleaf species is limited. In 2018–2020, cover crop experiments were conducted in South Dakota at 11 site-years. Cover crops were planted in the summer after small grains harvest as mixtures of dominantly grasses or broadleaves, a 50/50 grass/broadleaf mixture, and a no cover crop control. Soil and above-ground plant residue samples were collected in the fall before winter termination and in the spring before corn planting. Soil samples were analyzed for permanganate oxidizable carbon, potentially mineralizable nitrogen, and soil respiration. Fall and spring above-ground plant biomass in the cover crop plots were similar to the no cover crop control plots in seven of 11 site-years. Thus, growing cover crop mixes may accelerate decomposition of above-ground plant residue, possibly due to higher microbial diversity and activity under cover crops. However, including cover crops regardless of the mixture did not improve selected biological soil health indicators. Weather and soil properties (precipitation, soil organic matter, and pH) were related to differences in soil health measurements among site-years. Overall, in the first year of planting a multi-species mixture of grasses and/or broadleaves after small grain harvest, growers should not expect to find differences in soil health measurements. Long-term trials are needed to determine whether these different cover crop mixtures change soil health over time

Cover Crops Did Not Improve Soil Health but Hydroclimatology May Guide Decisions Preventing Cash Crop Yield Loss

Introduction: Cover crop (CC) is an essential tool to improve or maintain soil health, potentially improving cash crop productivity. Several recent reports of cash crop yield reduction following cover cropping necessitated this research to guide efficient CC decisions in the season before corn (Zea mays) or soybean (Glycine max) is to be grown.Methods: Therefore, we designed this multi-year, multi-location study to include the farmers who plant CC following the harvest of a small grain crop, majorly wheat (Triticum aestivum) or oats (Avena sativa), and then grow corn or soybean cash crop in the subsequent season. We also selected the farmers who used a fall CC mix that was winter-terminated, to avoid further complexities. The major objective of this study was to document soil health changes and cash crop yields following CC in eight selected locations around SD for three consecutive CC seasons between 2017-2020. Experimental plots were laid out at the farmercooperators’ CC fields, where no cover (NC) ‘control’ was tested against CC in a randomized complete block design (RCBD). Soil samples were analyzed for selected soil health indicators (SHIs): potentially mineralizable nitrogen (PMN), permanganate oxidizable carbon (POXC), soil respiration (SR), soil microbial biomass (SMB), soil nitrate-nitrogen, soil organic matter (SOM), and other basic soil properties (pH, electrical conductivity, etc.); crop and residue biomass were calculated, and cash crop economic yields were measured.Results and discussion: No statistically significant (p30 g kg-1). These findings directed us to investigate hydroclimatological parameters and climatological indices such as accumulated precipitation, standardized precipitation index (SPI), and standardized precipitation-evapotranspiration index (SPEI) for their impact on CC’s influence on cash crop yields.Conclusion: Our analyses indicated that hydroclimatology, especially SPEI for the month before CC planting can be used as a tool to guide successful CC decisions, reducing the risk of cash crop yield loss. Further investigations with SPI and SPEI, along with other climatological parameters are needed to explore and design better CC management tools

The challenges of tuberculosis control in protracted conflict: the case of Syria

Objectives Syria’s protracted conflict has resulted in ideal conditions for the transmission of tuberculosis (TB) and the cultivation of drug resistant strains. This paper compares TB control in Syria before and after the conflict using available data, examines the barriers posed by protracted conflict and those specific to Syria, and discusses what measures can be taken to address the control of TB in Syria. Results Forced mass displacement and systematic violations of humanitarian law have resulted in overcrowding and has destroyed key infrastructures leading to an increased risk of both sensitive and drug resistant TB while restricting the ability to diagnose, contact trace, treat and follow up. Pre-conflict, TB in Syria was officially reported at 22 per 100,000 population; the official figure for 2017 of 19 per 100,000 is likely a vast underestimate given the challenges and barriers to case detection. Limited diagnostics also affects the diagnosis of multidrug and rifampicin resistant TB which is reported at 8.8% of new diagnoses in 2017. Conclusion Control of TB in Syria requires a multipronged, tailored and pragmatic approach to improve timely diagnosis, increase detection, stop transmission and mitigate the risk of drug resistance. Solutions must also consider vulnerable populations such as imprisoned and besieged communities where the risk of drug resistance is particularly high and must recognize the limitations of national programming. Strengthening capacity to control TB in Syria with particular attention to these factors will positively impact other parallel conditions; this is key as attention turns to post-conflict reconstruction

A CNN Approach for Emotion Recognition via EEG

Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature extraction and machine learning techniques, which often struggle to capture intricate emotional nuances and may require extensive handcrafted feature engineering. To address these limitations, we propose a novel approach utilizing convolutional neural networks (CNNs) for EEG emotion recognition. Unlike traditional methods, our CNN-based approach learns discriminative cues directly from raw EEG signals, bypassing the need for intricate feature engineering. This approach not only simplifies the preprocessing pipeline but also allows for the extraction of more informative features. We achieve state-of-the-art performance on benchmark emotion datasets, namely DEAP and SEED datasets, showcasing the superiority of our approach in capturing subtle emotional cues. In particular, accuracies of 96.32% and 92.54% were achieved on SEED and DEAP datasets, respectively. Further, our pipeline is robust against noise and artefact interference, enhancing its applicability in real-world scenarios