Agricultural Output Research Articles

Soil fertility in mixed crop-livestock farming systems of Punjab, Pakistan: The role of institutional factors and sustainable land management practices

CONTEXTSoil salinization is a significant environmental challenge prevalent in arid and semi-arid regions, causing adverse effects on crop yields and jeopardizing household food security. Previous research has examined the influence of institutional dynamics and the adoption of sustainable land management practices in bolstering agricultural output, and some have investigated the interplay among socioeconomic determinants, institutional frameworks, and the uptake of climate-resilient and sustainable methodologies, or the association between soil health and agricultural productivity. Yet, there has been a lack of studies that considered this relationship altogether and their role in enhancing soil fertility. OBJECTIVEWe investigate the relationship among socioeconomic and institutional factors, adoption of sustainable land management practices, and the resulting changes in soil fertility between 2016 and 2019 within the context of mixed crop-livestock farms in the three irrigated agroecological zones of Punjab, Pakistan. A household survey was conducted in 2019 to complement soil attributes data collected by the Government of Punjab in 2016. Households that implemented sustainable land management practices between 2016 and 2019 were analysed to investigate the effect of various factors on soil fertility, including the adoption of sustainable land management practices and their influence on soil fertility dynamics. METHODSA structural equation model was employed to examine the relationship among exogenous variables, moderating variables, and endogenous variables, to explain their collective influence on soil fertility between the two periods. RESULTS AND CONCLUSIONSThe key findings highlight the significant role of institutional factors, including access to formal information channels such as extension services, demonstration trials, and credit facilities, alongside secure land rights, in predicting the adoption of sustainable land management practices such as gypsum application, laser land leveling, farmyard manuring, and agroforestry. Furthermore, specific SLM practices, particularly agroforestry featuring intercropping with Acacia spp. and farmyard manure application, exhibited positive impacts on change in soil organic matter, albeit with agroforestry showing a negative influence on soil phosphorus levels. The adoption of gypsum and LLL displayed positive effects on soil phosphorus levels over time, contrasting with LLL's adverse impact on soil organic matter. SIGNIFICANCEOur findings suggest that soil fertility benefits from policies enacted through public-private partnerships that lead to improved access to sustainable land management information, reduced credit barriers, establishment of local soil testing facilities, and expedited land entitlement processes. These findings highlight the critical role of collaborative partnerships and institutional arrangements in enhancing agricultural productivity and environmental sustainability.

A Review: CNN Based Plant Disease Detection

Abstract: Identifying plant diseases is the initial step toward mitigating losses in agricultural product yield and quantity. This work focuses on exploiting such technical breakthroughs, with an actual focus on plant disease detection. The system will analyze plant photos using cutting-edge image processing algorithms to precisely diagnose diseases, gauge the extent of damage, and make wise suggestions for necessary pesticides and nutrient supplements. The suggested remedy will identify particular nutrient deficits causing problems with crop health in addition to detecting the existence of illnesses. The system will aid farmers in making well-informed decisions about how to use nutrients and pesticides by integrating a thorough analysis. This will ultimately help to manage crop diseases in a timely and efficient manner, maximize agricultural yield, and reduce financial losses

Adaptation strategies of paddy farmers to build resilience to climate change in kahama district, Tanzania

The study investigates the adaptation strategies used by the paddy farmers in Kahama District to increase their adaptability to climate change. The study focuses on four main objectives, including changed farming practices, changes in cropping practices, strategies used to adapt to climate change, and desired interventions to improve agricultural production and resilience to climate change. A mixed-methods strategy is used in the study to collect both quantitative and qualitative data. According to the data, paddy farmers in Kahama District have used a number of climate change adaptation strategies. These adaptations include modifications to irrigation techniques, timing adjustments for planting, seed variety changes, and fertilizer treatment adjustments. The study also reveals a change in crop systems, with farmers choosing drought-tolerant cultivars, intercropping, and crop diversification; increased water management practices, increased soil conservation practices, and informed decision-making through the use of climate information services are some of the strategies used by paddy farmers to adapt to climate change. The study also identifies the interventions that farmers want to see implemented, such as enhanced irrigation systems, workshops on climate-resilient farming techniques, access to resilient seed varieties, and dependable weather forecasting and climate information services. These findings show the paddy farmers in the Kahama District's proactive response to the problems posed by climate change and help to better understand the adaptation measures they have used. The study highlights the importance of assisting farmers in advancing their current strategies and knowledge while also attending to their unique requirements. Implementing desired actions, such as expanding climate information services, supplying training and access to robust seed varieties, and enhancing irrigation infrastructure, can significantly increase agricultural productivity and the region's resistance to climate change. For politicians, extension services, and development organizations looking to promote climate change adaptation and sustainable agriculture, these research findings have real-world applications. Paddy farmers' ability to adapt, agricultural output, and livelihood security in the face of climate change problems can all be improved by matching interventions and policies with their recognized needs and goals. Such activities can support attempts to achieve food security and sustainable development in the Kahama District and other areas, as well as the agricultural sector's overall resilience.

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Multi-disciplinary strategy to optimize irrigation efficiency in irrigated agriculture

Equilibrium among water, food, energy, and climate actions is necessary for life to exist, quality, and sustainability. This article explored how to ensure sustainability, and equilibrium in the irrigation processes by proposing irrigation equilibrium indicators (IEIs) for sustainable irrigated agriculture (SIA). The primary purpose of IEIs is to achieve a state of sustainable climate and environmental balance. The pressures driving agriculture and irrigation professionals to enhance the irrigation scheme performance are tremendous in all agricultural communities. Monitoring, assessment, and improvement of agriculture practices and irrigation schemes for enhancing the Climate, water, food, and energy (CWFE) nexus is a must. As an auspicious climate action, IEIs were developed to enhance the irrigation scheme’s efficiency, within the scope of SIA. Subsequently, water, agricultural, food, and energy productivity could be optimized. Then, the appropriate equilibrium indicators could identify the actual performance of the CWFE nexus as a whole and the performance of each component. The effective irrigation scheme is the backbone of SIA. IEIs could measure the degree of achieving the overall and specific objectives and designated irrigation processes. The ultimate measure of equilibrium is optimizing sustainable agricultural yields and productivity, ensuring environmental balance, strengthening life quality, and maximizing economic returns.

Crop Recommendation System Using Machine Learning Algorithm

This study aims to develop an intelligent agricultural yield recommendation framework leveraging the capabilities of AI algorithms. The proposed framework takes yield efficiency and optimal growing seasons as crucial factors in generating appropriate crop recommendations. We have put forth four widely used models, namely Linear Regression (LR) and Multi-Layer Perceptron (MLP), which were trained and evaluated on a comprehensive dataset comprising historical agricultural data encompassing various features such as climatic factors, soil properties, and geographical variables. Furthermore, the data was segmented based on seasonal patterns to provide crop suggestions tailored to specific time periods. The performance of these models was assessed using standard metrics, and an ensemble approach was considered to enhance the system's robustness. Ultimately, the developed framework offers farmers and agricultural professionals a valuable tool for making informed decisions, optimizing crop selection, and enhancing overall agricultural productivity.

Unifying Forces: Harnessing Blockchain and Artificial Intelligence Integration for Enhanced Innovation

Blockchain and artificial intelligence have now become the emerging technologies of our time. However, both strategies induce a change in the market but differ in terms of how creative and complex they are. While block chain is decentralized; distributed ledger technology that the one stores information in multiple locations with no centralized monitoring system, on the other hand, Artificial Intelligence (AI) emulates human problem-solving ability as well as decision-making abilities using software, data or even robots. These two technologies combined together to bring about new opportunities such as productivity gain from both advantages of blockchain and AI security and transparency. Businesses can transform significantly through integration of AI with blockchain technology for data protection, clear data trails and general efficiency. Cyber security is one of the most important concepts linked to these two technologies. The possibility to combine AI’s capabilities with blockchain’s reliable and decentralized nature can be used to manage resources as well as decision making at educational institutes or within healthcare agencies; it may also be employed by society to influence agriculture outputs, planning urban dwellings among others. Artificial intelligence (AI) can help identify risks at an early stage whereas information security and integrity might vanish.

Exploring Climate-Induced Agricultural Risk in Saudi Arabia: Evidence from Farming Communities of Medina Region

Agriculture is vital to the Saudi Arabian economy since it provides food and other necessities to people living in rural areas, as well as those living in adjacent cities. Notwithstanding its considerable economic importance, the agricultural sector is confronted with formidable obstacles due to climate change, such as elevated temperatures, floods, extreme droughts, and decreased agricultural yields. Building a farming system capable of being climate resilient requires the comprehension of the vulnerabilities of farm households and related systems. This paper deals with the potential agricultural risks resulting from climate change in Saudi Arabia. In addition to concentrating on precipitation and temperature, the present research incorporates the three main components of the Climate Change Vulnerability Index (CCVI): sensitivity, exposure, and adaptive capability. The results of this research reveal a notable challenge that farmers in Saudi Arabia encounter, as they are very susceptible to the impacts of climate change. The Climate Change Vulnerability Index (CCVI) has a score of 0.730, signifying a substantial degree of vulnerability. The farmers in this region are very susceptible to uncertainties caused by climate change, as indicated by the significant exposure score of 0.725. This exposure comprises a wide range of concerns resulting from fluctuations in temperature, patterns of rainfall, and occurrences of severe weather, all of which have an immediate and negative effect on agriculture. Farmers in that region are more susceptible to the effects of climate change, which could jeopardize their livelihoods and agricultural operations, as shown by the susceptibility component (SVI) of 0.559. Moreover, the adaptive capacity (AVI) score of 0.567 highlights the fact that farmers have limited access to resources, skills, and experience, hindering their ability to tackle the challenges that have been caused by climate change in this region successfully. The results emphasize the immediate necessity for specific policies and assistance to enhance the resilience of agricultural communities in the area, as well as to mitigate the potential adverse effects of climate change on their livelihoods.

The Potential of Burkholderia sp. from Zea mays Roots as Plant Growth Promoting Rhizobacteria

Plant Growth-Promoting Rhizobacteria (PGPR) plays a crucial role in enhancing plant health, while Burkholderia sp. has been identified and has potential as a promising PGPR for maize plants. Recognizing the essential role of PGPR in plant health, this study explores how L-TRP addition might improve PGPR performance for better plant growth. Employing a completely randomized design, the study assesses the effects of Burkholderia sp. as PGPR on maize growth across three treatment groups: PGPR with L-TRP, PGPR without L-TRP, and a control group. The evaluation focuses on plant growth metrics, plant hormone production (auxin and gibberellin), and siderophore activity to gauge plant iron availability. Statistical analysis highlights that L-TRP supplementation notably increases auxin production in the PGPR + L-TRP group, surpassing the PGPR without L-TRP and control groups. Although both PGPR treatments elevate gibberellin levels compared to the control, auxin increase is the most significant outcome, indicating no substantial difference in gibberellin levels between the two PGPR groups. Enhanced siderophore production suggests improved iron assimilation for plants. The findings demonstrate that L-TRP supplementation with PGPR, particularly Burkholderia sp., effectively boosts maize growth, primarily through increased auxin and siderophore production. This combination presents a promising strategy for augmenting agricultural yields, especially for maize productivity, by leveraging the synergistic effects of soil microbes and nutrient supplementation.

Artificial Intelligence in Water Management for Sustainable Farming: A Review

Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.

CROP IMPROVEMENT THROUGH DIFFERENT MEANS TO ADDRESS CLIMATE CHANGE AND FOOD SECURITY

This study evaluated several crop development techniques in addition to the effects of climate change on productivity in agriculture. Climate affects agricultural productivity and the distribution of crops worldwide. Given the current and projected variations in temperature, precipitation, and carbon dioxide concentrations, it is unclear whether agroecosystems will be able to produce enough food to meet the world's needs. Crop output, water productivity, and soil water balance are frequently altered by climate change. Soil moisture content and groundwater levels will be affected by the immediate effects of global warming-induced changes in temperature and precipitation patterns. Agricultural output is affected by several factors, such as crop variety, growing circumstances, soil deterioration, and water availability. Modern agricultural systems use genetics, biotechnology, plant breeding, environmental monitoring, and adjustments to management techniques to adapt to climate change. On the other hand, without a more thorough approach to agricultural systems, development cannot proceed. A strategy like this would mean growing amounts of a wider variety of superior crops and advancing equity, sustainability, nutrition, and food security in the future.

A NAC transcription factor represses a module associated with xyloglucan content and regulates aluminum tolerance.

The transcriptional regulation of aluminum (Al) tolerance in plants is largely unknown, although Al toxicity restricts agricultural yields in acidic soils.. Here, we identified a NAM, ATAF1/2, and cup-shaped cotyledon 2 (NAC) transcription factor that participates in Al tolerance in Arabidopsis (Arabidopsis thaliana). Al substantially induced the transcript and protein levels of ANAC070, and loss-of-function anan070 mutants showed remarkably increased Al sensitivity, implying a beneficial role of ANAC070 in plant tolerance to Al toxicity. Further investigation revealed that more Al accumulated in the roots of anac070 mutants, especially in root cell walls, accompanied by a higher hemicellulose and xyloglucan level, implying a possible interaction between ANAC070 and genes that encode proteins responsible for the modification of xyloglucan, including xyloglucan endo-transglycosylases/hydrolase (XTH) or ANAC017. Yeast one hybrid analysis revealed a potential interaction between ANAC070 and ANAC017, but not for other XTHs. Furthermore, dual-luciferase reporter assay, RT-qPCR, and GUS analysis revealed that ANAC070 could directly repress the transcript levels of ANAC017, and knockout of ANAC017 in the anac070 mutant partially restored its Al sensitivity phenotype, indicating that ANAC070 contributes to Al tolerance mechanisms other than suppression of ANAC017 expression. Further analysis revealed that the core transcription factor SENSITIVE TO PROTON RHIZOTOXICITY1 (STOP1) and its target genes, which control Al tolerance in Arabidopsis, may also be involved in ANAC070-regulated Al tolerance. In summary, we identified a transcription factor, ANAC070, that represses the ANAC017-XTH31 module to regulate Al tolerance in Arabidopsis.

Climate‐driven runoff variability in semi‐mountainous reservoirs of the Vietnamese Mekong Delta: Insights for sustainable water management

AbstractThe Mekong Delta, South East Asia's ‘rice bowl’, sustains more than 18 million people through its agricultural output. This yield is secured by efficient water management systems but is susceptible to climatic changes. As Vietnam's policies aim to optimize the delta's semi‐mountainous regions reliant on rain‐fed agriculture, this study investigates drought risks and climate change impacts on runoff in the O Ta Soc and O Tuk Sa reservoirs, An Giang Province, Vietnam. Using simulation models, we determined runoff volumes for specific rainfall return periods and climate scenarios for the 2030s and 2050s. Using the storm water management model (SWMM), we simulated the reservoir water balance considering rainfall, evaporation and infiltration. Our findings suggest potentially increased runoff and reservoir storage due to intensified monsoons and reduced off‐season rainfall. The 4.77 km2 drainage of the O Ta Soc reservoir could benefit from this, while the 2.55 km2 drainage of the O Tuk Sa watershed may require alternative water‐sourcing strategies. This research offers insights for drought predictions, flood management and water strategies in An Giang. To refine these predictions, future research should consider upcoming rainfall patterns.

Effect of Covid-19 on Female-Headed Farming Household Food Security in Northern Bahr El Ghazal State, South Sudan

At the beginning of 2020, global food security was interrupted by the unprecedented occurrence of the COVID-19 pandemic and its associated restrictions to contain it. In South Sudan, women were among the most vulnerable groups due to restrictions and standard operating procedures (SoPs). This study was conducted to determine the effect of COVID-19 on the food security of female-headed households from three counties in Northern Bahr el Ghazal state, South Sudan. The analysis of the food security status was based on the Consolidated Approach for Reporting Indicators of Food Security (CARI) and food security was viewed in terms of availability of, and accessibility to food among households. Findings revealed that only 15.5% of the female-headed households sampled were food secure while 84.5% of them were food insecure on the 2-level scale. There was a significant reduction in average household income, size of land under agricultural production and crop harvest. Households lost access to produce markets, supermarkets, agro-input markets and produce stores, lowering their food production potential and farm output. Coping strategies included sale of household items, finding other work to supplement their agricultural activities, revision of household food consumption tendencies, new sales channels, while some of the households did nothing. These strategies are indicators of poverty, hunger and food insecurity which can lead to malnutrition. The study concluded that food security among female-headed households is low post-COVID, pushing women into life-threatening coping strategies in South Sudan. Development efforts in the country should be directed towards supporting recovery and resilience systems among these households in the country

Crop Monitoring System Using IOT

Currently, the low agricultural output is causing farmers to suffer. Low crop growth is primarily caused by knowledge of soil fertility and crop selection, even though proper crop selection is the primary booster key for enhancing agricultural yield by doing soil analysis and taking metrological elements into consideration. Farmers with limited experience in conventional farming are finding it difficult to make wise crop selection choices in the current environment. The low soil fertility is caused by the same crop being chosen for each seasonal cycle. The goal of this research is to use IoT devices to create an accurate and efficient system. Compared to the outdated manual laboratory testing procedures, which are subject to human errors, such a system is predictable. A crop's proper selection is mostly important in the agricultural field. As a contribution, we offer Smart Crop Selection (SCS), an IOT-based approach that is based on soil and metrological factor data. These variables include soil moisture content, temperature, and precipitation. Because these issues are not given as much care as they should, existing IoT-based systems are less efficient than our proposed model. Real-time sensory data is transmitted to Firebase cloud for analysis in the suggested model

Detecting Environmental Conditions in Cultivation Lands Using Bionic Devices

Climate change has an impact on crops, fruits, vegetables, and pest infestation, hence agricultural output is a top priority for most nations. As a result, professional growers have the problem of reaching maximum output results, and greenhouses have emerged as an excellent choice for ensuring these results. Farmers can employ innovative technologies inside greenhouses to prevent insect damage to plants and increase indoor growth through climate management. However, in order to manage agricultural fields and greenhouses successfully, farmers must now use Industry 4.0 technology such as robotics, Internet of Things devices, machine learning software, and so on. In this setting, sensor deployment is critical for gathering data and obtaining knowledge to help farmers make decisions. As a practical option for small farms, this research proposes an autonomous robot that drives along greenhouse crop paths with previously specified routes and can collect environmental data provided by a wireless sensor network when the farmer has no prior knowledge of the crop. An unsupervised learning method is used to cluster the optimal, standard, and deficient sectors of a greenhouse in order to identify improper crop development patterns. Finally, a user interface is built to assist farmers in planning the robot's route and distance while gathering sensor data to monitor crop conditions.

Crop yields fail to rise in smallholder farming systems in sub-Saharan Africa

Drawing on a harmonized longitudinal dataset covering more than 55,000 smallholder farms in six African countries, we analyze changes in crop productivity from 2008 to 2019. Because smallholder farmers represent a significant fraction of the world's poorest people, agricultural productivity in this context matters for poverty reduction and for the broader achievement of the UN Sustainable Development Goals. Our analysis measures productivity trends for nationally representative samples of smallholder crop farmers, using detailed data on agricultural inputs and outputs which we integrate with detailed data on local weather and environmental conditions. In spite of government commitments and international efforts to strengthen African agriculture, we find no evidence that smallholder crop productivity improved over this 12-y period. Our preferred statistical specification of total factor productivity (TFP) suggests an overall decline in productivity of -3.5% per year. Various other models we test also find declining productivity in the overall sample, and none of them finds productivity growth. However, the different countries in our sample experienced varying trends, with some instances of growth in some regions. The results suggest that major challenges remain for agricultural development in sub-Saharan Africa. They complement previous analyses that relied primarily on aggregate national statistics to measure agricultural productivity, rather than detailed microdata.

In-vitro induced drought and heat stress on seed germination of diverse African sorghum germplasm

Abstract One of the critical growth and development stages that is vulnerable to drought and heat stress even in hardy staple crops is germination. Sorghum is a critically essential, resilient, and diverse crop that displays genotypic variations in its ability to withstand such harsh conditions, limiting crop stand and ultimately leading to yield losses. Therefore, the goal of this study was to evaluate the performance of 50 high potential genotypes of African sorghum, including landraces, breeding lines and check varieties to simulated drought and heat stress at germination stage. The study used a split plot arrangement for temperature treatments, laid in a completely randomized design with three replications. Final germination percentage, mean germination time, germination index and coefficient variation of germination time (CVt) were determined. Data was subjected to generalized linear model, principal component analysis, hierarchical agglomerative cluster analysis and principal coordinate analysis to determine statistical differences in genotypes and visualize groups of genotypes according to their overall performance in assessed germination parameters. Drought stress and supra-optimal temperatures suppressed and delayed germination. The genotypes were grouped into six distinct clusters based on their performance. Genotypes NPGRC1593, NPGRC1782, NPGRC1476 and IS224426 performed exceptionally well under both stressors and outperformed check varieties in almost all parameters assessed. To improve crop establishment and increase agricultural yields, breeding and crop improvement programs should focus on genotypes that can withstand both stresses.

Analysis of Tobacco Straw Return to the Field to Improve the Chemical, Physical, and Biological Soil Properties and Rice Yield

Straw incorporation into soil contributes significantly to the sustainable development of agriculture. To investigate the impact of tobacco straw returns on a tobacco–rice replanting system, we designed an experiment with two straw return levels and a control group: T1 (full return), T2 (root return), and CK (no straw return). Over a three-year field experiment in rice fields in South China, we assessed the effects of tobacco straw return on soil quality, microbial diversity, dry matter accumulation, and yield composition of rice. The results demonstrated that returning tobacco straw to the field effectively enhanced rice yield by positively influencing various soil physical, chemical, and biological properties. Compared to those in the CK treatment, as the soil porosity increased from 9.0% to 12.4%, the mean weight diameter of the soil aggregates substantially increased, ranging from 28.7% to 45.2%. There were significant increases in soil organic matter, total nitrogen, and alkaline dissolved nitrogen. Soil sucrase activity increased between 29.8% and 44.9%, and urease activity increased between 4.3% and 62.2% over the three consecutive years of straw return. The diversity index of soil fungi significantly increased. Additionally, rice yield increased markedly, ranging from 1.8% to 5.1%. Overall, the enhancement effect of T1 surpassed that of T2. According to our comprehensive analysis, the incorporation of tobacco straw into the field was found to enhance the physical properties of the soil, elevate soil enzyme activity, and increase the abundance of soil microorganisms. Consequently, this practice led to improved rice yield and a reduction in agricultural waste output. Overall, the return of tobacco straw to the field represents a clean and dependable approach in rice-cultivated tobacco areas to improve soil health and rice productivity.

Metagenomic sequencing and reconstruction of 82 microbial genomes from barley seed communities

Barley (Hordeum vulgare) is essential to global food systems and the brewing industry. Its physiological traits and microbial communities determine malt quality. Although microbes influence barley from seed health to fermentation, there is a gap in metagenomic insights during seed storage. Crucially, elucidating the changes in microbial composition associated with barley seeds is imperative for understanding how these fluctuations can impact seed health and ultimately, influence both agricultural yield and quality of barley-derived products. Whole metagenomes were sequenced from eight barley seed samples obtained at different storage time points from harvest to nine months. After binning, 82 metagenome-assembled genomes (MAGs) belonging to 26 distinct bacterial genera were assembled, with a substantial proportion of potential novel species. Most of our MAG dataset (61%) showed over 90% genome completeness. This pioneering barley seed microbial genome retrieval provides insights into species diversity and structure, laying the groundwork for understanding barley seed microbiome interactions at the genome level.