Combining slow-release fertilizer and plastic film mulching reduced the carbon footprint and enhanced maize yield on the Loess Plateau.

Agricultural practices significantly contribute to greenhouse gas (GHG) emissions, necessitating cleaner production technologies to reduce environmental pressure and achieve sustainable maize production. Plastic film mulching is commonly used in the Loess Plateau region. Incorporating slow-release fertilizers as a replacement for urea within this practice can reduce nitrogen losses and enhance crop productivity. Combining these techniques represents a novel agricultural approach in semi-arid areas. However, the impact of this integration on soil carbon storage (SOCS), carbon footprint (CF), and economic benefits has received limited research attention. Therefore, we conducted an eight-year study (2015-2022) in the semi-arid northwestern region to quantify the effects of four treatments [urea supplied without plastic film mulching (CK-U), slow-release fertilizer supplied without plastic film mulching (CK-S), urea supplied with plastic film mulching (PM-U), and slow-release fertilizer supplied with plastic film mulching (PM-S)] on soil fertility, economic and environmental benefits. The results revealed that nitrogen fertilizer was the primary contributor to total GHG emissions (≥71.97%). Compared to other treatments, PM-S increased average grain yield by 12.01%-37.89%, water use efficiency by 9.19%-23.33%, nitrogen accumulation by 27.07%-66.19%, and net return by 6.21%-29.57%. Furthermore, PM-S decreased CF by 12.87%-44.31% and CF per net return by 14.25%-41.16%. After eight years, PM-S increased SOCS (0-40 cm) by 2.46%, while PM-U decreased it by 7.09%. These findings highlight the positive effects of PM-S on surface soil fertility, economic gains, and environmental benefits in spring maize production on the Loess Plateau, underscoring its potential for widespread adoption and application.

Study on the multifunctional spatial-temporal evolution and coupling coordination of cultivated land: A case study of Hebei Province, China.

The rational use of cultivated land can guarantee food security and thus is highly important for ensuring social stability, economic development and national security. The current study investigated the multifunctional temporal and spatial variation characteristics of cultivated land and explored the spatial and temporal characteristics of the multifunction and coupling coordination degrees of cultivated land throughout Hebei Province. Based on the administrative division data, statistical yearbook data and land use status data of the impacted areas, a multifunctional evaluation index system of cultivated land was established. The CRITIC weight method and entropy weight method were used to determine the weight of the index, the comprehensive index model was used to determine the production, social security, ecology and landscape functions of cultivated land of Hebei Province in different periods, the coupling coordination model was used to explore the multifunctional coupling coordination degree of cultivated land in each county, and spatial autocorrelation analysis was performed to determine the correlation of the multifunctional coupling coordination degrees. From 2000 to 2020, the production, social security and landscape function of cultivated land in Hebei Province trended upward; the ecological function trended slightly downward. The multifunctional coupling coordination degree of cultivated land in Hebei Province trended significantly upward and changed from limited coordination to intermediate coordination. Furthermore, it exhibited strong agglomeration and a significant positive spatial correlation, forming a 'V'-type change rule of first decreasing and then increasing. Hebei Province exhibited remarkable spatial and temporal characteristics of the multifunction and coupling coordination degrees of cultivated land. Regions could thus customize different cultivated land functions to maximize the benefits of cultivated land use. The findings of this study may provide a scientific basis and theoretical support for sustainably using and managing cultivated land resources in areas with similar human geographical environments.

The potential climate benefits of seaweed farming in temperate waters.

Seaweed farming is widely promoted as an approach to mitigating climate change despite limited data on carbon removal pathways and uncertainty around benefits and risks at operational scales. We explored the feasibility of climate change mitigation from seaweed farming by constructing five scenarios spanning a range of industry development in coastal British Columbia, Canada, a temperate region identified as highly suitable for seaweed farming. Depending on growth rates and the fate of farmed seaweed, our scenarios sequestered or avoided between 0.20 and 8.2 Tg CO2e year-1, equivalent to 0.3% and 13% of annual greenhouse gas emissions in BC, respectively. Realisation of climate benefits required seaweed-based products to replace existing, more emissions-intensive products, as marine sequestration was relatively inefficient. Such products were also key to reducing the monetary cost of climate benefits, with product values exceeding production costs in only one of the scenarios we examined. However, model estimates have large uncertainties dominated by seaweed production and emissions avoided, making these key priorities for future research. Our results show that seaweed farming could make an economically feasible contribute to Canada's climate goals if markets for value-added seaweed based products are developed. Moreover, our model demonstrates the possibility for farmers, regulators, and researchers to accurately quantify the climate benefits of seaweed farming in their regional contexts.

Redesigning and validation of fertilizer use in maize for variable plant densities in central rift valley and Jimma in Ethiopia.

Due to low adoption and sub-optimal fertilizer use and planting density recommendation in maize, redesigning and testing these technologies are required. The study was conducted to evaluate redesigned fertilizer use of maize in two pant densities (32,443 and 53,333 plants ha-1 in Central Rift Valley (CRV); 27724 and 62,000 plants ha-1 in Jimma) on farmers' fields in contrasting agro-ecologies of Ethiopia. The on-farm study was conducted in the 2017 and 2018 cropping seasons with 3 × 2 fertilizer and plant density, factors in both regions of Ethiopia. In redesigned fertilizer use, nutrients were estimated based on the target yield. In this study, 40.8, 0.0, and 12.2 kg ha-1 N, P, and K were estimated for the redesigned fertilizer use in CRV (50% of water-limited potential yield (Yw) = 3.1 t ha-1) whereas in Jimma (50% of Yw = 7.5 t ha-1) 149.8, 9, 130.6 kg ha-1 N, P and K were estimated to produce the 50% of Yw. Linear mixed modeling was used to assess the effect of fertilizer-plant density treatments on maize yield and nutrient use efficiency. The result revealed that the average estimated maize yield for WOF, FFU, and RDFU fertilizer treatments were 2.6, 3.6, and 4.5 t ha-1 under current plant density (32,443 plants ha-1) in CRV whereas the average yields of these treatments were 3.2, 4.5 and 4.5 t ha-1 respectively when maize was grown with redesigned plant density (53,333 plants ha-1) in the same location. The average maize yield with WOF, FFU, and RDFU were 3.0, 4.6, and 4.6 t ha-1 with 27,774 plants ha-1 plant density in Jimma whereas the average maize yields over the two seasons with the same treatments were 4.3, 6.0 and 8.0 t ha-1 respectively when the crop is planted with 62,000 plants ha-1 plant density. The RDFU and redesigned plant density resulted in significantly higher yield compared to their respective control CRV but RDFU significantly increased maize yield when it was planted at redesigned (62,000 plant ha-1) in Jimma. FFU and RDFU were economically viable and redesigned plant density was also a cheaper means of improving maize productivity, especially in the Jimma region. Soil organic carbon and N were closely related to the grain yield response of maize compared to other soil factors. In conclusion, this investigation gives an insight into the importance of redesigned fertilizer use and redesigned plant density for improving maize productivity and thereby narrowing the yield gaps of the crop in high maize potential regions in Ethiopia like Jimma.

A critical systematic review on spectral-based soil nutrient prediction using machine learning.

The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quality, necessitating soil nutrient analysis for enhancing farm productivity and environmental sustainability. Researchers increasingly turn to Artificial Intelligence (AI) techniques to improve crop yield estimation and optimize soil nutrition management. This study reviews 155 papers published from 2014 to 2024, assessing the use of machine learning (ML) and deep learning (DL) in predicting soil nutrients. It highlights the potential of hyperspectral and multispectral sensors, which enable precise nutrient identification through spectral analysis across multiple bands. The study underscores the importance of feature selection techniques to improve model performance by eliminating redundant spectral bands with weak correlations to targeted nutrients. Additionally, the use of spectral indices, derived from mathematical ratios of spectral bands based on absorption spectra, is examined for its effectiveness in accurately predicting soil nutrient levels. By evaluating various performance measures and datasets related to soil nutrient prediction, this paper offers comprehensive insights into the applicability of AI techniques in optimizing soil nutrition management. The insights gained from this review can inform future research and policy decisions to achieve global development goals and promote environmental sustainability.

Agronomic performance and technological quality of sugarcane submitted to different poultry litter dosages.

Sugarcane is a central crop for sugar and ethanol production. Investing in sustainable practices can enhance productivity, technological quality, mitigate impacts, and contribute to a cleaner energy future. Among the factors that help increase the productivity of sugarcane, the physical, chemical and biological parameters of the soil are amongst the most important. The use of poultry litter has been an important alternative for soil improvement, as it acts as a soil conditioner. Therefore, this work aimed to verify the best doses of poultry litter for the vegetative, reproductive and technological components of sugarcane. The experiment was carried out at Usina Denusa Destilaria Nova União S/A in the municipality of Jandaia, GO. The experimental design used was a complete randomized block design with four replications: 5 × 4, totaling 20 experimental units. The evaluated factor consisted of four doses of poultry litter plus the control (0 (control), 2, 4, 6 and 8 t ha-1). In this study, were evaluated the number of tillers, lower stem diameter, average stem diameter, upper stem diameter, plant height, stem weight and productivity. The technological variables of total recoverable sugar, recoverable sugar, Brix, fiber, purity and percentage of oligosaccharides were also evaluated. It was observed, within the conditions of this experiment, that the insertion of poultry litter did not interfere significantly in most biometric, productive and technological variables of the sugarcane. But it can also be inferred that there was a statistical trend toward better results when the sugarcane was cultivated with 4 t ha-1 of poultry litter.

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI.

Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers' ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.

Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM.

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.

Simulating adaptive grazing management on soil organic carbon in the Southeast U.S.A. using MEMS 2.

Grazing lands play a significant role in global carbon (C) dynamics, holding substantial soil organic carbon (SOC) stocks. However, historical mismanagement (e.g., overgrazing and land-use change) has led to substantial SOC losses. Regenerative practices, such as adaptive multi-paddock (AMP) grazing, offer a promising avenue to improve soil health and help combat climate change by increasing SOC accrual, both in its particulate (POC) and mineral-associated (MAOC) organic C components. Because adaptive grazing patterns emerge from the combination of different levers such as frequency, intensity, and timing of grazing, studying AMP grazing management in experimental trials and representing it in models remains challenging. Existing ecosystem models lack the capacity to predict how different adaptive grazing levers affect SOC storage and its distribution between POC and MAOC and along the soil profile accurately. Therefore, they cannot adequately assist decision-makers in effectively optimizing adaptive practices based on SOC outcomes. Here, we address this critical gap by developing version 2.34 of the MEMS 2 model. This version advances the previous by incorporating perennial grass growth and grazing submodules to simulate grass green-up and dormancy, reserve organ dynamics, the influence of standing dead plant mass on new plant growth, grass and supplemental feed consumption by animals, and their feces and urine input to soil. Using data from grazing experiments in the southeastern United States and experimental SOC data from two conventional and three AMP grazing sites in Mississippi, we tested the capacity of MEMS 2.34 to simulate grass forage production, total SOC, POC, and MAOC dynamics to 1-m depth. Further, we manipulated grazing management levers, i.e., timing, intensity, and frequency, to do a sensitivity analysis of their effects on SOC dynamics in the long term. Our findings indicate that the model can represent bahiagrass forage production (BIAS = 9.51 g C m-2, RRMSE = 0.27, RMSE = 65.57 g C m-2, R2 = 0.72) and accurately captured the dynamics of SOC fractions across sites and depths (0-15 cm: RRMSE = 0.05; 15-100 cm: RRMSE = 1.08-2.07), aligning with patterns observed in the measured data. The model best captured SOC and MAOC stocks across AMP sites in the 0-15 cm layer, while POC was best predicted at-depth. Otherwise, the model tended to overestimate SOC and MAOC below 15 cm, and POC in the topsoil. Our simulations indicate that grazing frequency and intensity were key levers for enhancing SOC stocks compared to the current management baseline, with decreasing grazing intensity yielding the highest SOC after 50 years (63.7-65.9 Mg C ha-1). By enhancing our understanding of the effects of adaptive grazing management on SOC pools in the southeastern U.S., MEMS 2.34 offers a valuable tool for researchers, producers, and policymakers to make AMP grazing management decisions based on potential SOC outcomes.

Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize-Soybean Rotation in Nebraska.

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize-soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N0 parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (fb). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m-2 (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community.

Temporal variations in absorption and translocation of heavy metal(loid)s in pak choi (Brassica rapa L.) under open-field and greenhouse cultivation.

Elucidating the absorption and translocation of heavy metal(loid)s by common vegetables across different growth environments and stages is crucial for conducting accurate environmental risk assessments and for associated control. This study investigated temporal variations in the absorption and translocation capacities of pak choi (Brassica rapa L.) for As, Cd, Cr, Cu, Pb, and Zn in polluted soils during the plant growth cycle under greenhouse and open-field cultivation modes. Results showed high root metal(loid) bioconcentration factors and root-to-shoot translocation factors for Cd (0.25 and 1.44, respectively) and Zn (0.26 and 1.01), but low values for As (0.06 and 0.88) and Pb (0.06 and 0.87). The Cd concentration in the aerial edible parts peaked during the early slow growth period, whereas other heavy metal(loid)s peaked during the later stable maturity period. Root bioconcentration and root-to-shoot translocation factors did not significantly differ between cultivation modes. However, greenhouse cultivation exhibited lower average Cd and Zn concentrations in the edible parts and cumulative uptake amounts of most metal(loid)s than open-field cultivation during the typical harvest period spanning days 60 and 90. Short-term transitioning from open-field to greenhouse cultivation may reduce health risks associated with heavy metal(loid) intake via pak choi consumption. These findings facilitate sustainable agricultural practices and food safety management.

Introducing qrlabelr: Fast user-friendly software for machine- and human-readable labels in agricultural research and development.

The advent of modern tools in agricultural experiments, digital data collection, and high-throughput phenotyping have necessitated field plot labels that are both machine- and human-readable. Such labels are usually made with commercial software, which are often inaccessible to under-funded research programs in developing countries. The availability of free fit-for-purpose label design software to under-funded research programs in developing countries would address one of the main roadblocks to modernizing agricultural research. The goal was to develop a new open-source software with design features well-suited for field trials and other agricultural experiments. We report here qrlabelr, a new software for creating print-ready plot labels that builds on the foundation of an existing open-source program. The qrlabelr software offers more flexibility in the label design steps, guarantees true string fidelity after QR encoding, and provides faster label generation to users. The new software is available as an R package and offers customizable functions for generating plot labels. For non-R users or beginners in R programming, the package provides an interactive Shiny app version that can be launched from R locally or accessed online at https://bit.ly/3Sud4xy. The design philosophy of this new program emphasizes the adoption of best practices in plot label design to enhance reproducibility, tracking, and accurate data curation in agricultural research and development studies.

The impact of farmers' participation in green cooperative production on green performance-A study based on the moderating effect of environmental regulation.

Based on 491 farmers joining in cooperatives microscopic data in Jiangxi Province,the paper uses Ordinary Least Squares to test the influence mechanism of cooperative green production on green performance, and takes environmental regulation as a regulatory variable to explore the relationship between cooperative green production and cooperative green performance. The results have shown that: (1) The green production cooperatives have a significant positive impact on their green performance, and the impact of green production on economic performance, social performance and ecological performance gradually strengthens from weak to strong; (2) Environmental regulations have a positive regulatory effect on the relationship between cooperative green production and cooperative green performance, among which three types of environmental regulations, namely, incentive, restraint and guided, can strengthen the positive relationship between green production and green performance.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

The Discovery of Highly Efficient and Promising ABA Receptor Antagonists for Agricultural Applications Based on APAn Modification.

Abscisic acid (ABA) is one of the many naturally occurring phytohormones widely found in plants. This study focused on refining APAn, a series of previously developed agonism/antagonism switching probes. Twelve novel APAn analogues were synthesized by introducing varied branched or oxygen-containing chains at the C-6' position, and these were screened. Through germination assays conducted on A. thaliana, colza, and rice seeds, as well as investigations into stomatal movement, several highly active ABA receptor antagonists were identified. Microscale thermophoresis (MST) assays, molecular docking, and molecular dynamics simulation showed that they had stronger receptor affinity than ABA, while PP2C phosphatase assays indicated that the C-6'-tail chain extending from the 3' channel effectively prevented the ligand-receptor binary complex from binding to PP2C phosphatase, demonstrating strong antagonistic activity. These antagonists showed effective potential in promoting seed germination and stomatal opening of plants exposed to abiotic stress, particularly cold and salt stress, offering advantages for cultivating crops under adverse conditions. Moreover, their combined application with fluridone and gibberellic acid could provide more practical agricultural solutions, presenting new insights and tools for overcoming agricultural challenges.

Nitrous oxide (N2O) emission characteristics of farmland (rice, wheat, and maize) based on different fertilization strategies.

Fertilizer application is the basis for ensuring high yield, high quality and high efficiency of farmland. In order to meet the demand for food with the increasing of population, the application of nitrogen fertilizer will be further increased, which will lead to problems such as N2O emission and nitrogen loss from farmland, it will easily deteriorate the soil and water environment of farmland, and will not conducive to the sustainable development of modern agriculture. However, optimizing fertilizer management is an important way to solve this problem. While, due to the differences in the study conditions (geographical location, environmental conditions, experimental design, etc.), leading to the results obtained in the literatures about the N2O emission with different nitrogen fertilizer application strategies have significant differences, which requiring further comprehensive quantitative analysis. Therefore, we analyzed the effects of nitrogen fertilizer application strategies (different fertilizer types and fertilizer application rates) on N2O emissions from the fields (rice, wheat and maize) based on the Meta-analysis using 67 published studies (including 1289 comparisons). For the three crops, inorganic fertilizer application significantly increased on-farm N2O emissions by 19.7-101.05% for all three; and organic fertilizer increased N2O emissions by 28.16% and 69.44% in wheat and maize fields, respectively, but the application of organic fertilizer in rice field significantly reduced N2O emissions by 58.1%. The results showed that overall, the application of inorganic fertilizers resulted in higher N2O emissions from farmland compared to the application of organic fertilizers. In addition, in this study, the average annual temperature, annual precipitation, soil type, pH, soil total nitrogen content, soil organic carbon content, and soil bulk weight were used as the main influencing factors of N2O emission under nitrogen fertilizer strategies, and the results of the study can provide a reference for the development of integrated management measures to control greenhouse gas emissions from agricultural soils.

The application of varying amount of green manure combined with nitrogen fertilizer altered the soil bacterial community and rice yield in karst paddy areas.

Long-term application of green manure (GM) and nitrogen (N) fertilizers markedly improved soil fertility and boosted rice yield in ecologically fragile karst paddy fields. However, the precise response mechanisms of the soil bacterial community to varying amounts of green manure alone and in combination with N fertilizer in such environments remain poorly elucidated. In this study, we investigated the soil bacterial communities, keystone taxa, and their relationship with soil environmental variables across eight fertilization treatments. These treatments included group without N addition (N0M0, no N fertilizer and no GM; N0M22.5, 22.5 t/ha GM; N0M45, 45 t/ha GM, N0M67.5, 67.5 t/ha GM) and group with N addition (NM0, N fertilizer and no GM; NM22.5, N fertilizer and 22.5 t/ha GM; NM45, N fertilizer and 45 t/ha GM; NM67.5, N fertilizer and 67.5 t/ha GM). The results revealed that increasing green manure input significantly boosted rice yield by 15.51-22.08% and 21.84-35% in both the group without and with N addition, respectively, compared to N0M0 treatment. Moreover, with escalating green manure input, soil TN, AN, AK, and AP showed an increasing trend in the group without N addition. However, following the addition of N fertilizer, TN and AN content initially rose, followed by a decline due to the enhanced nutrient availability for rice. Furthermore, the application of a large amount of N fertilizer decreased the C: N ratio in the soil, resulting in significant changes in both the soil microbial community and its function. Particularly noteworthy was the transition of keystone taxa from their original roles as N-fixing and carbon-degrading groups (oligotrophs) to roles in carbon degradation (copiotrophs), nitrification, and denitrification. This shift in soil community and function might serve as a primary factor contributing to enhanced nutrient utilization efficiency in rice, thus significantly promoting rice yield.

Wild rodents seed choice is relevant for sustainable agriculture.

Mitigating pre-harvest sprouting (PHS) and post-harvest food loss (PHFL) is essential for enhancing food securrity. To reduce food loss, the use of plant derived specialized metabolites can represent a good approach to develop a more eco-friendly agriculture. Here, we have discovered that soybean seeds hidden underground during winter by Tscherskia triton and Apodemus agrarius during winter possess a higher concentration of volatile organic compounds (VOCs) compared to those remaining exposed in fields. This selection by rodents suggests that among the identified volatiles, 3-FurAldehyde (Fur) and (E)-2-Heptenal (eHep) effectively inhibit the growth of plant pathogens such as Aspergillus flavus, Alternaria alternata, Fusarium solani and Pseudomonas syringae. Additionally, compounds such as Camphene (Cam), 3-FurAldehyde, and (E)-2-Heptenal, suppress the germination of seeds in crops including soybean, rice, maize, and wheat. Importantly, some of these VOCs also prevent rice seeds from pre-harvest sprouting. Consequently, our findings offer straightforward and practical approaches to seed protection and the reduction of PHS and PHFL, indicating potential new pathways for breeding, and reducing both PHS and pesticide usage in agriculture.

Humic acid and grafting as sustainable agronomic practices for increased growth and secondary metabolism in cucumber subjected to salt stress.

Salinity stress poses a significant treat to crop yields and product quality worldwide. Application of a humic acid bio stimulant and grafting onto tolerant rootstocks can both be considered sustainable agronomic practices that can effectively ameliorate the negative effects of salinity stress. This study aimed to assess the above mentioned ameliorative effects of both practices on cucumber plants subjected to saline environments. To attain this goal a factorial experiment was carried out in the form of a completely randomized design with three replications. The three factors considered were (a) three different salinity levels (0, 5, and 10 dS m-1 of NaCl), (b) foliar application of humic acid at three levels (0, 100, and 200 mg L-1), and (c) both grafted and ungrafted plants. Vegetative traits including plant height, fresh and dry weight and number of leaf exhibited a significant decrease under increasing salinity stress. However, the application of humic acid at both levels mitigated these effects compared to control plants. The reduction in relative water content (RWC) of the leaf caused by salinity, was compensated by the application of humic acid and grafting. Thus, the highest RWC (86.65%) was observed in grafting plants with 0 dS m-1 of NaCl and 20 mg L-1 of humic acid. Electrolyte leakage (EL) increased under salinity stress, but the application of humic acid and grafting improved this trait and the lowest amount of EL (26.95%) was in grafting plants with 0 dS m-1 of NaCl and 20 mg L-1 of humic acid. The highest amount of catalase (0.53 mmol H2O2 g-1 fw min-1) and peroxidase (12.290 mmol H2O2 g-1 fw min-1) enzymes were observed in the treatment of 10 dS m-1 of NaCl and 200 mg L-1 humic acid. The highest amount of total phenol (1.99 mg g-1 FW), total flavonoid (0.486 mg g-1 FW), total soluble carbohydrate (30.80 mg g-1 FW), soluble protein (34.56 mg g-1 FW), proline (3.86 µg g-1 FW) was in grafting plants with 0 dS m-1 of NaCl and 200 mg L-1 of humic acid. Phenolic acids and phenylalanine ammonia lyase (PAL) and polyphenol oxidase (PPO) enzymes increased with increasing salinity and humic acid levels. Contrary to humic acid, salt stress increased the sodium (Na+) and chlorine (Cl-) and decreased the amount of potassium (K+) and calcium (Ca2+) in the root and leaf of ungrafted cucumber. However, the application 200 mg L-1 humic acid appeared to mitigate these effects, thereby suggesting a potential role in moderating physiological processes and improving growth of cucumber plants subjected to salinity stress. According to the obtained results, spraying of humic acid (200 mg L-1) and the use of salt resistant rootstocks are recommended to increase tolerance to salt stress in cucumber. These results, for the first time, clearly demonstrated that fig leaf gourd a new highly salt-tolerant rootstock, enhances salt tolerance and improves yield and quality of grafted cucumber plants by reducing sodium transport to the shoot and increasing the amount of compatible osmolytes.