Optimum Yield Research Articles

The analysis of classical, polynomial regression and cubic spline mathematical models in hemp biodiesel optimization: an experimental comparison

Post-pandemic inflationist pressures, climate changes and extremes, regional conflicts, and soaring food prices caused the food crisis to increase rapidly worldwide. This global problem directs producers and researchers to use oils used as feedstock in biodiesel production effectively. In this context, it is important to assay the transesterification parameters and conduct new optimization studies to increase biodiesel yield. In this study, methyl ester was produced from hemp oil by transesterification using sodium hydroxide (NaOH). Next, classical optimization study was carried out to determine the effects of catalyst amount, alcohol:oil molar ratio, reaction temperature, and reaction time variables on biodiesel yield. Secondly, the cubic spline mathematical model (CSMM) and polynomial regression mathematical model (PRMM) were applied to the first data of this optimization. Among these optimization methods, the utmost biodiesel yield registered was 96.115% at hemp seed oil (HSO):methanol molar ratio of 5.59:1, catalyst concentration of 0.531 wt%, reaction temperature of 42.5 °C, reaction time of 62.1 min, and agitation intensity of 600 rpm at PRMM. Some vital fuel properties obtained from HSO biodiesels as a result of three optimizations satisfied the EN 14214 standard. The results illustrated that the optimal yields from CSMM and PRMM are 0.765% and 1.065% higher, respectively, according to the maximum efficiency obtained from the classical optimization. The outcomes showed that CSMM and PRMM are cost-effective, easy to handle, and promising new approaches.

Population Dynamics of the Common Carp (Cyprinus carpio L. 1758) Stock in Lake Arekit, Ethiopia: Implications for Management and Conservation

Cyprinus carpio is a widely distributed and commercially important fish species in Ethiopia. Effective management is essential to sustain fisheries and provide benefits to local communities. This study aims to estimate the growth, mortality, and exploitation rate of C. carpio using length-frequency data. A total of 194 C. carpio specimens (117 females and 77 males) were collected between June 2022 and May 2023. The population parameters were determined using FISAT (ELEFAN I) software. The Von Bertalanffy growth function estimations were L∞ = 51.45 cm total length, K = 1.1 year−1; t0 = −0.124 year, and growth performance index (Φ′) = 3.464. The total mortality rate (Z), natural mortality rate (M), and fishing mortality rate (F) were 2.55 year−1, 1.58 year−1, and 0.97 year−1, respectively. The estimated potential longevity (tmax) and average age at which C. carpio attains length at the optimum cohort biomass or yield per recruit (Lopt = 34.79) in Lake Arekit were calculated as 2.60 years and 1.29 years, respectively. The Z/K ratio (2.32), exploitation rate (E = F/Z = 0.38), and highest permissible yield per recruit for C. carpio (Emax = 0.421) suggest enhancing fishery production by increasing the exploitation rate of the underexploited C. carpio in Lake Arekit. The results of this study provide information on the stock of C. carpio, which can be used to guide management efforts. The fish catch in Lake Arkeit is currently far lower than it could be. Therefore, the fish harvest should be boosted through the use of permitted nets, the establishment of groups of fishermen who can catch fish effectively, and the linking of the benefits of fish farming’s supply chain to the potential of fisheries.

Genome-scale metabolic model of the versatile bacterium Paracoccus denitrificans Pd1222.

A genome scale metabolic model of the bacterium Paracoccus denitrificans has been constructed. The model containing 972 metabolic genes, 1,371 reactions, and 1,388 unique metabolites has been reconstructed. The model was used to carry out quantitative predictions of biomass yields on 10 different carbon sources under aerobic conditions. Yields on C1 compounds suggest that formate is oxidized by a formate dehydrogenase O, which uses ubiquinone as redox co-factor. The model also predicted the threshold methanol/mannitol uptake ratio, above which ribulose biphosphate carboxylase has to be expressed in order to optimize biomass yields. Biomass yields on acetate, formate, and succinate, when NO3- is used as electron acceptor, were also predicted correctly. The model reconstruction revealed the capability of P. denitrificans to grow on several non-conventional substrates such as adipic acid, 1,4-butanediol, 1,3-butanediol, and ethylene glycol. The capacity to grow on these substrates was tested experimentally, and the experimental biomass yields on these substrates were accurately predicted by the model.IMPORTANCEParacoccus denitrificans has been broadly used as a model denitrifying organism. It grows on a large portfolio of carbon sources, under aerobic and anoxic conditions. These characteristics, together with its amenability to genetic manipulations, make P. denitrificans a promising cell factory for industrial biotechnology. This paper presents and validates the first functional genome-scale metabolic model for P. denitrificans, which is a key tool to enable P. denitrificans as a platform for metabolic engineering and industrial biotechnology. Optimization of the biomass yield led to accurate predictions in a broad scope of substrates.

Machine Learning Techniques for Crop Yield Forecasting in Semi-Arid (3A) Zone, Rajasthan (India)

Economic growth and prosperity of a nation are inextricably linked to the agricultural sector. In the compass of agriculture, climate and other environmental changes are one of the main challenges. The present study attempts to predict crop yield for the Jaipur district which is an important region in the semi-arid eastern plain of Rajasthan (India). Machine learning (ML) techniques are used in forecasting and developing practical solutions for numerous challenges such as climate change with other environmental factors. Crop yield prediction is the process of predicting yield using historical data through meteorological parameters and past yield records. This paper used the agrometeorological time-series data from the year 1991 to 2020 for optimal yield forecasting. There have been numerous attempts to improve crop yield prediction by employing machine learning techniques. However, in this study, fusing the intelligence of reinforcement with deep learning, we got a comprehensive framework for mapping raw data to crop prediction values, allowing an optimal estimation of crop yields with higher accuracy. Upon comparative analysis of numerous ML algorithms, Random Forest is found the best-performing algorithm with an accuracy of 92.3% using supervised machine learning methods. With an accuracy of 92.3%, the proposed Random Forest-based model outperforms other techniques that are currently being used to predict crop yields. The study predictions could significantly help in choosing the best cropping pattern and planning for action accordingly. The results provide the best ways to solve environmental and agricultural problems in this semi-arid region of the specified Rajasthan state facing climate change issues.

Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels

Chlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological adaptation mechanisms, offer a basis for improving agricultural production, and contribute to ecosystem management. This study involved the collection of hyperspectral data, image data, and SPAD data from jujube leaves. These data were then processed using SG smoothing and the isolated forest algorithm, following which eigenvalues were extracted using a combination of Pearson’s phase relationship method and the Partial Least Squares Regression–continuous projection method. Subsequently, seven methods were employed to analyze the results, with hyperspectral data and color channel data used as independent variables in separate experiments. The findings indicated that the integrated BPNN-RF-Ridge Regression algorithm provided the best results, with an R2 of 0.8249, MAE of 2.437, and RMSE of 2.9724. The inclusion of color channel data as an independent variable led to a 3.2% improvement in R2, with MAE and RMSE increasing by 1.6% and 3.9%, respectively. These results demonstrate the effectiveness of integrated methods for the determination of chlorophyll content in jujube leaves and underscore the potential of using multi-source data to improve the model fit with a minimal impact on errors. Further research is warranted to explore the application of these findings in precision agriculture for jujube yield optimization and income-related endeavors, as well as to provide insights for similar studies in other plant species.

Assessment of Ailanthus altissima seed oil as a potential source for biodiesel production using nickel oxide nanoparticles catalyst

The identification of suitable non-edible plant feedstock is crucial not only to meet energy demands but also to be environmentally friendly. This study aimed to synthesize nickel oxide (NiO) nanoparticles and use these for synthesizing biodiesel from the non-edible feedstock Ailanthus altissima. The confirmation of NiO synthesis and its characteristics were studied using XRD and SEM. The biodiesel synthesis and its physicochemical characteristics were confirmed using GC–MS, NMR, and FT-IR spectroscopies. The fuel characteristics were examined using ASTM standard procedures. The results confirmed that the nanoparticles were crystalline, spherical to cuboidal in shape, and had an average diameter of 24 nm. The suitable set of parameters for the optimum biodiesel yield (95 %) was the oil-to-methanol ratio of 1:24, 25 mg of catalyst, 60 °C temperature, and a reaction time of 90 min. The majority of the fuel's characteristics fell within ASTM D-6751 acceptable ranges. 14 distinct kinds of FAMEs are present, according to the GC–MS results. The FT-IR spectra confirmed the successful synthesis of biodiesel, which displayed significant peaks for several functional groups. 1H NMR and 13C NMR spectrums confirm the conversion of biodiesel synthesis. The environmental friendliness of AAB (Ailanthus altissima biodiesel) and its competitiveness as a feedstock for biodiesel production on a commercial scale are confirmed by its physicochemical properties.

Automatic Modeling Prediction Method of Nitrogen Content in Maize Leaves Based on Machine Vision and CNN

Existing maize production is grappling with the hurdles of not applying nitrogen fertilizer accurately due to subpar detection accuracy and responsiveness. This situation presents a significant challenge, as it has the potential to impact the optimal yield of maize and ultimately, the profit margins associated with its cultivation. In this study, an automatic modeling prediction method for nitrogen content in maize leaves was proposed based on machine vision and convolutional neural network. We developed a program designed to streamline the image preprocessing workflow. This program can process multiple images in batches, automatically carrying out the necessary preprocessing steps. Additionally, it integrates an automated training and modeling system that correlates the images with nitrogen content values. The primary objective of this program is to enhance the accuracy of the models by leveraging a larger dataset of image samples. Secondly, the fully connected layer of the convolutional neural network was reconstructed to transform the optimization goal from classification based on 0–1 tags into regression prediction, so that the model can output numerical values of nitrogen content. Furthermore, the prediction model of nitrogen content in maize leaves was gained by training many samples, and samples were collected in three key additional fertilizing stages throughout the growth period of maize (i.e., jointing stage, bell mouth stage, and tasseling stage). In addition, the proposed method was compared with the spectral detection method under full-wave band and characteristic wavelengths. It was verified that our machine vision and CNN (Convolutional Neural Network)-based method offers a high prediction accuracy rate that is not only consistently better—by approximately 5% to 45%—than spectral detection approaches but also features the benefits of easy operation and low cost. This technology can significantly contribute to the implementation of more precise fertilization practices in maize production, leading to potential yield optimization and increased profitability.

Catalytic Performance Investigation of Alkali and Bifunctional Catalysts Derived from Lignocellulosic Biomasses for Biodiesel Synthesis from Waste Frying Oil.

In this study, alkali and bifunctional catalysts were synthesized for waste frying oil methyl ester (WFOME) synthesis. Coffee husk (CH) and CH blended with Eragrostis tef straw (TS) (CH-TS) lignocellulosic biomasses (LBs) were utilized during the catalysts' synthesis. The alkali catalysts were CH and CH-TS ashes, both modified by KNO3 impregnation. They are designated as C-45 and C-Mix, respectively. Zirconia (ZrO2) promoted CH ash catalysts via precipitation followed by impregnation (Bic-PP) and in situ precipitation-impregnation (Bic-Dm) were the bifunctional ones. CH and CH-TS chars were the supporting frameworks during the catalysts' composite materials (CCMs) preparation. The combustion performance of LBs and CCMs was evaluated and associated with the catalysts' physicochemical properties. Using XRD, SEM, FTIR, alkalinity, TOF, and BET surface area analysis, catalysts were characterized. The combustion performance of the LBs was in the order of TS > CH-TS > CH. Among CCMs, the highest combustion performance was for CCM-Mix (KNO3/(CH-TS char)) and the lowest was for CCM-45 (KNO3/ CH char). The C-Mix catalyst was a light green powder due to the reaction between inorganic components, whereas C-45 was dark gray due to the presence of unburned char. The CCMs for bifunctional catalysts had moderate combustion performance and yielded light gray powdered catalysts containing tetragonal ZrO2. The optimum WFOME yields were 98.08, 97, 92.69, and 93.05 wt % for C-Mix, C-45, Bic-Dm, and Bic-PP assisted WFO transesterification, respectively. The results were obtained at a reaction temperature of 65 °C, time of 1 h, and methanol to WFO molar ratio of 15:1 using catalyst amounts of 5 and 7 wt % for the alkali and bifunctional catalysts, respectively. The greatest moisture resistance was offered by the C-Mix catalyst. The best reusability was for the C-45 catalyst. Catalysts' deactivation modes include active site leaching and poisoning.

ПРИМЕНЕНИЕ КЛАСТЕРНОГО АНАЛИЗА ДЛЯ ОПРЕДЕЛЕНИЯ СЕЛЕКЦИОННОЙ ЦЕННОСТИ ЧЕЧЕВИЦЫ (Lens culinaris Medik)

The article presents the results of a study of agronomic characters of lentil, carried out in the fields of the A.I. Barayev Scientific and production center for grain farming JSC in 2021-2023. The objects of the study were 100 varieties from the genetic collections of lentil from the Institute of Plant Industry, ICARDA and foreign varieties (Turkey, Canada, Bulgaria, Moldova, Ukraine, Belarus). The Shyraily variety was adopted as a standard for large-seeded lentils, and the Krapinka variety – for small-seeded lentils. As a result of the research, sources of certain agronomic characters of lentils in the conditions of the Northern Kazakhstan were identified. Hierarchical clustering of the main components based on important agronomic characters revealed the presence of five groups with different breeding values. The most promising in practical and breeding terms are the samples belonging to the first cluster, which exhibit the highest expression of such quantitative characters as optimal yield and seed weight per plant. The second cluster includes productive and earlymaturing samples, while the samples in the third cluster can be used as sources of high protein content. Lentil samples from the fourth and fifth clusters may serve as promising parent material for the development of new lentil varieties.

Deep insight : Mathematical modeling and statistical analysis for mango leaf disease classification using advanced deep learning models

This paper presents a comprehensive investigation into the mathematical modeling and statistical analysis of classification of mango leaf diseases employing state-of-the-art Deep Learning models. The literature review underscores the significance of automated disease detection in agriculture, with Convolutional Neural Networks (CNNs) emerging as pivotal tools for image-based tasks. The proposed methodology encompasses meticulous preprocessing, optimal hyperparameter tuning, and fine-tuning of pretrained models (Inception V3, MobileNet V3 Small, MobileNet V3 Large, and ResNet50). Results indicate rapid convergence and outstanding accuracy during initial training, with all models achieving 100% accuracy on both validation and test datasets. K-Fold cross-validation affirms the models’ consistency, with Inception V3 demonstrating leading performance. Detailed analyses, including training and loss graphs and confusion matrices, offer nuanced insights and highlight areas for refinement, particularly in distinguishing Healthy leaf, Gall Midge, and Anthracnose. This research positions the proposed methodology as a promising approach with potential applications in real-world agricultural scenarios, where precise disease detection is critical for effective crop management and optimal yields.

Detecting Plant Diseases at Scale: A Distributed CNN Approach with PySpark and Hadoop

In modern agriculture, detecting and diagnosing plant diseases are pivotal for crop health and yield optimization. This transformative initiative leverages cutting-edge technology to reshape the field. Data integrity is paramount for our machine learning model's success. We meticulously collect verified data from Kaggle, then preprocess the dataset of 2000 diverse images. Uniformly resizing them to 150px and batching them in groups of 32 optimizes training speed and memory usage.The model's innovation lies in its Convolutional Neural Networks (CNNs) powered by PySpark for efficient distributed data processing. Integration of Tkinter bridges technology and user-friendliness, offering farmers intuitive disease detection solutions through a graphical interface. This triad of CNNs, PySpark, and Tkinter achieves an outstanding average accuracy of 95.76%, adept at distinguishing diseased from healthy leaves. Embracing this innovation promises to elevate agricultural practices and enhance crop productivity, marking a milestone in modern agriculture.

Artificial intelligence on the agro-industry in the United States of America

<p>Integrating artificial intelligence (AI) into agriculture is a pivotal solution to address the pressing challenges posed by rapid population growth and escalating food demand. Traditional farming methods, unable to cope with this surge, often resort to harmful pesticides, deteriorating soil health. However, the advent of AI promises a transformative shift toward sustainable agricultural practices. In the context of the United States, AI's historical trajectory within the agricultural sector showcases a remarkable evolution from rudimentary applications to sophisticated systems focused on optimizing production and quality. The future of American agriculture lies in AI-driven innovations, spanning various facets such as image sensing for yield mapping, labor management, yield optimization, and decision support for farmers. Despite its numerous advantages, the deployment of AI in agriculture does not come without challenges. This paper delved into both the benefits and drawbacks of AI adoption in the agricultural domain, examining its impact on the agro-industry and the environment. It scrutinized the emergence of robot farmers and AI's role in reshaping farming practices while acknowledging the inherent problems associated with AI implementation, including accessibility, data privacy, and potential job displacement. Moreover, the study explored how AI tools can catalyze the development of agribusiness, offering insights into overcoming existing challenges through innovative solutions. By comprehensively understanding the opportunities and obstacles entailed in AI integration, stakeholders can navigate the agricultural landscape adeptly, fostering a more sustainable and resilient food system for future generations.</p>

Sustainable Agriculture Practices: AI and IoT's Vital Contribution

The evolving landscape of agriculture has undergone a significant shift towards a technology-driven and data-centric approach, mainly propelled by the advent of Artificial Intelligence (AI) and the Internet of Things (IoT). This short review talks about numerous studies exploring the transformative impact of these cutting-edge technologies in sustainable agriculture practices. It outlines how the traditional statistical methods have given way to a more data-driven and quantitative approach, reshaping every facet of farming. The article emphasizes the immense potential of IoT in revolutionizing agriculture, shedding light on the challenges encountered when integrating these technologies into conventional farming systems. It explores the diverse applications of sensors and IoT devices across various agricultural stages, from land preparation and irrigation systems to pest control and harvesting techniques, while also highlighting the integration of advanced tools like unmanned aerial vehicles (UAVs) for crop monitoring and yield optimization. Overall, it emphasizes the urgency to embrace these innovative technologies, addressing key challenges for their seamless integration into traditional farming and aquaculture practices to ensure a sustainable and efficient future for agriculture.

Harnessing precision agriculture technologies to enhance efficiency and yield in the agro- industrial complex

Precision agriculture (PA) technologies have revolutionized farming practices, offering substantial benefits in terms of efficiency and yield optimization within the agro-industrial complex. This article explores the integration of various PA tools, including geographic information systems (GIS), global positioning systems (GPS), remote sensing, and data analytics, to enhance agricultural management practices. By leveraging these technologies, farmers can make informed decisions tailored to specific field conditions, thereby optimizing resource allocation and minimizing environmental impact. The article delves into case studies and empirical evidence demonstrating the tangible advantages of PA adoption across diverse agricultural sectors, such as crop cultivation, livestock management, and agroforestry. Furthermore, it examines the role of emerging technologies, such as unmanned aerial vehicles (UAVs) and sensor networks, in furthering the precision and scalability of agricultural operations. Key findings highlight the potential of PA to improve resource use efficiency, reduce input costs, mitigate risks associated with climate variability, and ultimately increase agricultural productivity and profitability. The synthesis of this research underscores the transformative impact of PA technologies in fostering sustainable and resilient agro-industrial systems for the future.

Optimization of the Product Yields of the Pyrolysis of Palm Kernel Shell in a Fixed-Bed Reactor Using Response Surface Methodology (RSM)

Pyrolysis is a method used for the thermochemical conversion of biomass and agro wastes into three major by-products namely, biochar, bio-oil, and biogas in the absence of oxygen. Global waste generation is increasing at an alarming rate. Palm kernel shell (PKS) is among the palm wastes accumulating in many palm oil mills around the globe with the attendant problems in waste management. The utilization of coal for energy generation is more expensive and not environmentally friendly. Bio-oil refineries and industries require the utilization of minimum resources (input) to achieve optimal response (output) in the production process. However, more than 30% of the energy content derivation from the pyrolysis process is wasted due to nonoptimization of the process parameters. This study investigated the optimization of the product yields of the pyrolysis of PKS in a fixed-bed batch reactor using response surface methodology (RSM). Masses of 1 kg, 2 kg, and 3 kg were pyrolyzed in batches at process temperature variations of 200 OC, 300 OC, 400 OC, and 500 OC. Design Expert 12 software (Version 12.0.3.0 Stat-Ease Inc. MN, USA) and Analysis of Variance (ANOVA) were used for the statistical analysis. Lack-of-fit, adjusted, and predicted multiple correlation coefficients and coefficient of variation of the different polynomial models were compared to select the best-fitting polynomial model. The optimal feedstock parameters were 2.66 kg of PKS and 410.0 OC process temperature. The predicted results were 0.986 kg of biochar, 1.205 kg of bio-oil, 0.481 kg of biogas, 1116.5 cm3 of bio-oil, and 140.6 minutes. The experimental results were 0.894 kg of biochar, 1.316 kg of bio-oil, 0.498 kg of biogas, 1208.9 cm3 of bio-oil, and at 162.8 minutes through validation. The validated quadratic model was suitable for the optimization and RSM is a numerical, statistical, and mathematical tool for modeling and optimization of the pyrolysis process in a fixed-bed batch reactor.

Length-Weight Relationship and Population Dynamics of Fringescale Sardinella Sardinella fimbriata (Clupeiformes, Clupeidae) from Malampaya Sound, Palawan, Philippines

The study investigated the growth, mortality, exploitation rate and recruitment of Fringescale sardinella (Sardinella fimbriata Valenciennes, 1847) found in Malampaya Sound, Palawan, Philippines. A total of 1200 samples were collected from April 2023 to March 2024. The relationship between length and weight was expressed as y = 0.0291 x 2.561 for combined sex, y = 0.0404 x 2.4254 for males, and y = 0.0355 x 2.4856 for females. The resulting b values pinpoint to a negative allometric growth. The calculated regression model showed a significant association during which the p-values were below 0.0000 and the r2 values ranged between 0.76 and 0.79. Estimation using the FISAT II software resulted in an asymptotic length (L∞) = 16.28 cm, growth rate (K) = 0.68 yr-1, maximum length (Lmax) = 15.78 cm, theoretical age at birth (t0) = –0.5515 years and growth performance index (ɸ’) = 2.256. The estimated average total mortality rate (Z), natural mortality (M), and fishing mortality (F) were 2.69, 1.68, and 1.01 per year, respectively. Recruitment occurs throughout the year and peaks in June. It can be noted that the exploitation is calculated as 0.38, which is considered lightly exploited. To have a sustainable population and avoid overfishing, an optimum total yield (E50) can be set at an exploitation rate of 0.278. The maximum yield (Emax) is at an exploitation rate of 0.421.

Physiological and transcriptomic analysis provides novel insight into growth in Kam sweet rice under different nitrogen supplies

AbstractKam Sweet Rice (KSR) is a rice cultivar cultivated for thousands of years in China. Proper and balanced use of nitrogen sources is essential for achieving optimal growth and yield in KSR. This study was carried out to find the most effective hydroponic nutrient solution for cultivating and promoting the growth of KSR. Different nitrogen treatments were applied to KSR cultivars GouDan (GD), GouHa (GH), HongHe (HH), LeiLongGang (LG), and ZaiBianXiaoXiangHe (ZH), resulting in noticeable changes in both growth and physiological traits. Our findings revealed that D3 treatment [1.2 mM NH4Cl + 2.4 mM NaNO3+ neutral amino acids (0.1 mM Ser + 0.2 mM Thr + 0.2 mM Gly + 0.2 mM Pro +0.3 mM Ala)] had a notable positive impact on the growth of GH and ZH which showed the best performance in terms of plant height, root length and fresh weight when compared to the control (CK). Through transcriptome analysis, we discovered that these treatments altered the expression levels of genes associated with carbon metabolism, photosynthesis, and nitrogen pathways within 40 days. Interestingly, by altering the growth and tillering in rice transgenic plants, we validated the role of amino acid transporter OsAAP13 identified in transcriptome profiling, underscoring its significance. Our results are helpful for large‐scale production of KSR with the key methods for controlling the growth of KSR. It is a valuable asset for future investigations aimed at unravelling the intricate mechanisms governing plant growth in the context of different nitrogen supplies.

Land suitability assessment to achieve optimal field rice yield

Land suitability assessment to achieve optimal field rice yield

Integrated management of cheat (Bromus secalinus L.) and downy brome (Bromus tectorum L.) in Oklahoma grain-only winter wheat

Abstract In Oklahoma, downy brome and cheat are difficult-to-control winter annual grasses. In the past, cheat infested most of the winter wheat hectares harvested in Oklahoma. Biotypes that are cross-resistant to acetolactate synthase–inhibiting herbicides have left growers with minimal management options for conventional and herbicide-tolerant systems. Field trials near Lahoma, Oklahoma, in 2019–2020 and 2020–2021 evaluated integrated management of cheat and downy brome using three strategies: planting date (optimal, delayed, and late), cultivar selection (high and low competitiveness), and herbicide choice (no herbicide, sulfosulfuron at 35.2 g ai ha−1 and pyroxsulam at 18.4 g ai ha−1). Visual control, weed species present, wheat biomass at heading, and grain yield data were collected. In 2019–2020, 8 to 9 wk after treatment, visual control increased by 15% with the delayed planting compared with the optimal planting date and 14% with the late planting date. In 2020–2021, similar control (∼99%) was recorded for delayed and late plantings with 23% greater control than the optimal timing. Due to a lack of weed coverage, weed biomass in 2019–2020 had no response to planting date, cultivar, or herbicide treatment. Downy brome biomass during 2020–2021 was approximately 90% lower with delayed to late planting dates than the optimal planting date. In the same year, downy brome and cheat biomass were low (≤0.4 and 0.2 g m−2) and 98% less after an herbicide application than a nontreated area. Wheat grain yield at the optimal planting date was greater than yields from delayed and late plantings in 2019–2020. A delay in planting from the optimal date to delayed or late timings decreased wheat yield by 14% and 21%, respectively. In 2020–2021, wheat yield from the late planting was reduced by 57% compared with the optimal planting yield. Delaying the planting date and the use of a common herbicide can suppress cheat and downy brome, but a decline in wheat yield may occur.

Cu-modified InVO4 photocatalysts for enhanced N2 fixation using chemical reagents and electroplating sludge as the Cu source.

Inspired by simulation analysis, we found that Cu decoration could enhance the NH3 production rate of InVO4 through promoting N2 adsorption and reducing the activation energy of the key hydrogenation step. 5% Cu/InVO4 exhibited an optimal NH3 yield of 195.11 μmol gcat-1 h-1, approximately six times higher than that of InVO4. Cu/InVO4 was also fabricated by upcycling Cu from electroplating sludge, achieving a gratifying nitrogen fixation performance of 154.13 μmol gcat-1 h-1.