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Cultivation studies evaluating land-use histories and coevolutionary dynamics between humans and plants focus predominantly on domesticated species. Traditional anthropological divisions of "foragers" and "farmers" have shaped our understanding of ancient cultivation practices but have several limitations, including how people stewarded and managed nondomesticated species. To investigate the long-term effects of plant management in the Pacific Northwest, this study focuses on beaked hazelnut ( Corylus cornuta ) which has a long, precolonial history of management, transportation, and cultivation in British Columbia (BC, Canada). In particular, isolated hazelnut populations in northwestern BC are thought to be the result of historical transplanting and management. We sampled individual hazelnuts (n = 219) representing three distinct regions in and assessed 9,650 genome-wide SNPs identified with nextRAD genotyping-by-sequencing libraries to test for population genetic structure. We used linear measurements of individuals to assess morphological phenotypes and to identify variation between individuals and lineages. These data reveal shared genetic clusters in distant and disjunct northwestern and interior regions consistent with the movement of humans across the landscape. We also find several small genetically distinct populations in the northwestern region. The Genetic structure of hazelnut in the previously labeled "disjunct" region in Gitxsan, Ts'msyen, and Nis g a'a homelands is consistent with the enduring influence of people on the distribution of purportedly "wild" plant species. Our results support the hypothesis that hazelnut was likely transplanted long distances and also managed in situ. This study highlights the often-overlooked agency of Indigenous Peoples in shaping species range distributions in North America., Competing Interests: Competing interests statement:The authors declare no competing interest.

Sugar is the largest agricultural crop by mass and has seen a rapid increase in consumption around the world. There are widespread public health efforts to curb sugar intake through targeted policies given its association with noncommunicable diseases. Although curbing sugar intake aligns with sustainable diets that meet essential environmental and health targets, such a shift may be challenging from a political economy perspective. Utilizing sugar for other purposes such as the production of microbial protein, biofuels, and bioplastics, or using sugar lands to grow other food items, or rewilding could provide health and environmental win-wins that could be more politically palatable. Here, we explore several potential scenarios to illustrate the option space from which national and international stakeholders could choose locally appropriate pathways for alternative utilization of sugar or its lands. While beneficial, such alternative pathways would require the integration of environmental, economic, and health policies to provide a smoother diet transition that reduces stakeholder tensions. Given the trade in sugar as a commodity crop, international approaches that compensate sugar producers for avoided production or incentivize them for redirecting sugars to other uses will be needed. Such approaches could borrow concepts from Just Transition Partnerships that have been applied to energy system transitions in ensuring a transition for major exporters of sugar cash crops across low- and middle-income nations., Competing Interests: Competing interests statement:The authors declare no competing interest.

The importance for multi-dimensional priority-setting of agricultural innovations is growing, given that agricultural technologies usually play multiple roles for smallholder farmers. This study assesses agricultural technologies based on their multi-dimensional impacts in the drylands of sub-Saharan Africa and South Asia. The study applies the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to a set of promising agricultural technologies and uses three outcome criteria: the benefit-cost ratio, poverty reduction, and nutrition security. The technologies are related to important cereals and grain legumes grown in these regions: sorghum, pearl millet, and finger millet; groundnut, cowpea, chickpea, lentil, pigeon pea, and soybean. The results show that the top technologies based on individual criteria can differ from the top technologies identified using a combination of criteria. For example, in semi-arid southern Africa, a promising technology which involves integrated pest management for cowpea ranks among the top five technologies which can reduce poverty. However, the analysis involving TOPSIS shows that nutrition security is more important in that region compared to poverty. As such, the top 5 technologies with the highest multi-dimensional impact for semi-arid southern Africa do not involve a cowpea technology; rather, they are all related to pigeon pea, a nutritious grain legume which is currently more consumed in that region compared to cowpea. One limitation of this study is that it did not consider all the roles of agricultural technologies in the drylands of sub-Saharan Africa and South Asia; this should be considered in future studies involving TOPSIS or other MCDM techniques. Nevertheless, the study shows that TOPSIS can successfully be used for multi-dimensional ex-ante impact assessment of agricultural technologies, and thus can support the prioritization of investments targeting agricultural research for development., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gbegbelegbe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The integration of IoT (Internet of Things) devices has emerged as a technical cornerstone in the landscape of modern agriculture, revolutionising the way farming practises are viewed and managed. Smart farming, enabled by interconnected sensors and technologies, has surpassed traditional methods, giving farmers real-time, granular information into their farms. These Internet of Things devices are responsible for collecting and sending greenhouse data (temperature, humidity, and soil moisture) for the required destination, to provide a comprehensive awareness of environmental factors critical to crop growth. Therefore, ensuring that the received data are accurate is a challenge, thus this paper investigates the optimization of Agriculture IoT communication, proposing a complete strategy for improving data transmission efficiency within smart farming ecosystems. The proposed model intends to maximize energy efficiency and data throughput in the context of essential agricultural factors by using Lagrange optimization and a Deep Convolutional Neural Network (DCNN). The paper focus on the ideal communication required distance between IoT sensors that measure humidity, temperature, and water levels and central control systems. The investigation emphasizes the critical necessity of these data points in guaranteeing crop health and vitality. The proposed technique strives to improve the performance of agricultural IoT communication networks through the integration of mathematical optimization and cutting-edge deep learning. This paradigm change emphasizes the inherent link between precise achievable data rate and energy efficiency, resulting in resilient agricultural ecosystems capable of adjusting to dynamic environmental conditions for optimal crop output and health., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alturif et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The world aims to ensure environmental sustainability and consolidate agricultural factor productivity, yet the excruciating impact of climate change coincides and remains a persistent threat. Therefore, the study aims to estimate the technical efficiency of cereal crop yields and investigate the impacts of climate change on agricultural factor productivity. For this purpose, panel data from 35 sub-Saharan African countries between 2005 and 2020 was employed. For analysis, the pooled OLS and stochastic frontier models were employed. The results revealed that in the region, the average efficiency score for producing cereal crops between 2005 and 2020 was 83%. The stochastic frontier model results showed that labour contributed 51.5% and fertilizer contributed 5.7% to raising the technical efficiency of cereal crop yields, whereas arable land per hectare reduced the technical efficiency of cereal yields by 44.7%. The pooled OLS regression result showed that climate change proxies (CO2 and methane emissions) diminish land, labour, and fertilizers productivity at a 1% significance level, whereas GDP per capita boosts significantly the total factor productivity in agriculture. This confirmed how climate change reduced land, labour, and fertilizer input productivity. The results concluded that the region had a high level of technical efficiency; of which labour and fertilizer inputs contributed the largest share; however, their productivity has dwindled due to climate change. To increase cereal crop yield efficiency and limit the adverse effects of climate change on agricultural input productivity, the region should combine skilled and trained labour and fertilizer with sophisticated agriculture technologies, as well as adopt climate resistance technologies (weather- resistant variety seed and planting revolution mechanisms)., Competing Interests: the authors have no any computing interest, (Copyright: © 2024 Alemu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Water is a fundamental necessity for humans and a critical resource in agriculture. However, water scarcity poses a significant challenge, especially considering that agriculture accounts for a substantial portion of freshwater usage. The inadequate monitoring resources in agriculture lead to unnecessary wastage of water, affecting crop growth and the water supply. This challenge has spurred us to focus on the agricultural domain, aiming to conserve water by imparting intelligence into irrigation systems by converging IoT and machine learning technologies. Our study has proposed a Smart Irrigation System (SIS), designed to operate remotely, leveraging open-source IoT platforms. Utilizing IoT and machine learning methodologies can effectively optimize water usage in irrigation practices. The designed system comprises hardware and software tools, where comprehensive data is monitored through an application interface. The sensor's interface with a 32-bit microcontroller is used to gather data such as environmental temperature, humidity, and soil moisture and temperature. The user (farmer) continuously receives real-time updates about the condition of the farmland through the Blynk app. The app triggers a notification advising the user to start or stop watering based on pre-set threshold values of soil moisture. Additionally, users can control the water pump remotely through the app. The data collected from the deployed smart irrigation system was used to train a machine learning algorithm incorporating weather parameters and soil temperature to predict soil moisture content. This trained model aims to offer guidance for future assessments of unknown soil samples based on the weather conditions., Competing Interests: Declarations. All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The environmental and multi-sectoral challenges faced by small islands requires consideration of sustainability issues. The sustainability challenges in these regions involve in particular the achievement of a greater autonomy through the development of local resources. This is a complex system that encompasses interconnections between the resources available and the land use. In this article we focus on the study of the Water-Energy-Food (WEF) nexus, and propose an integrated and systemic approach to do so. Our contribution consists in studying food system sustainability of small islands by exploring the reciprocal influences between the valorization of local WEF resources and land use competition for various integrated WEF scenarios. Additionally, we integrate dietary behaviors and demonstrate their close interlinking with land use practices, and thus their impact on the potential for transitioning towards a more sustainable food system. To achieve this, we present a generic combined Geographical Information Systems (GIS) and robust optimization model. This model is then applied to Reunion island using collected real data. Our approach aims to assist local policymakers, at the island scale, by constructing insightful scenarios to facilitate informed decision-making. Our results highlight the need to save land space when developing local resources through effective land use management policies combined with a shift in food practices. This shift would imply in particular, to convert some of the sugarcane areas into subsistence farming. Furthermore, the results emphasize the importance of transitioning consumption practices under various integrated WEF scenarios, showcasing our model as an insightful decision-support tool., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Authier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Long-term continual straw return can enhance soil quality and increase crop yields by perpetually altering the soil environment. However, little is known about how different straw return methods affect soil physicochemical properties, enzymatic processes, and crop yields. The study aims to determine how different straw return practices improve soil structure, nutrients, enzyme activities, and maize yields. In this experiment, a field trial was conducted in 2021-2022 in the irrigated area of the Tumochuan Plain Irrigation District to determine the effects of four different straw returns on soil structure, nutrients, enzyme activities, soil quality, and maize yields. The four types of straw return included straw incorporation with deep tillage (DPR), straw incorporation with subsoiling (SSR), no-tillage mulching straw return, and farmer's shallow rotation (CK). Our results showed that DPR and SSR enhanced water retention capacity by reducing the bulk weight of the 0-45 cm soil layer. DPR and SSR significantly increased soil organic C (12.76%), total nitrogen (25.32%), and available nutrients (i.e. AP, NO 3 - -N) in the 0-45 cm soil layer compared to CK, whereas there were no differences between straw-returned treatments in the 0-15 cm soil layer. Finally, maize yield was significantly increased by 13.14% and 11.41% in the second year of DPR and SSR, respectively, compared to CK. This study demonstrated that DPR and SSR are effective at enhancing the agricultural utilization of crop residues and represent feasible strategies for improving physical, chemical, and biological processes in continuous maize cropping systems, leading to increased crop productivity., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Integrating native ornamental plants with substrate amended with lignocellulosic biomass and biochar in vertical sub-surface flow constructed wetlands offers a novel and effective approach to wastewater treatment. This study evaluates the potential of mesocosm constructed wetland systems using native ornamental plants (Canna indica, Lilium wallichianum, and Tagetes erecta) grown in substrates amended with lignocellulosic biomass and biochar. The influent and effluent were analyzed for pH, total dissolved solids (TDS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), phosphorus (PO 4 -P), and nitrogen forms, i.e., ammonia (NH 4 -N) and nitrate (NO 3 -N) for 5 weeks. Investigated mesocosms showed an average removal efficiency of 49.21% for BOD, 53.76% for COD, 40.64% for NH 4 -N, 41.76% for NO 3 -N, and 21.53% for PO 4 -P. Canna indica demonstrated the highest removal efficiencies, achieving 58.19% for BOD and 64.49% for COD, followed by Lilium wallichianum with 56.12% for BOD and 62% for COD, while Tagetes erecta showed lower efficiencies of 49.63% for BOD and 52.24% for COD. The result shows that the designed mesocosms are a promising nature-based alternative to the technologically complex and expensive conventional technologies, with numerous additional ecological benefits. This study also indicates that the locally available organic materials are effective substrate components for constructed wetlands and after their use in wetlands; these digested organic materials may further be used as an effective source of nutrient-rich fertilizers or soil amendments in agriculture., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Crop foraging by primates is a prevalent form of human-wildlife conflict, especially near protected areas. This behavior poses significant economic challenges for subsistence farmers, jeopardizing both livelihoods and conservation efforts. This study aimed to assess patterns of primate crop-foraging events and estimate maize damage in protected and unprotected fields in southern Ethiopia. Data were collected over 12 months between 2020 and 2021 in the Sodo Zuriya and Damot Gale districts of Southern Ethiopia. A team of six field experts and 25 farmers participated in the study, during which maize damage inflicted by primates was assessed using 25 deployed camera traps. Linear mixed models were used to explore the relationship between maize damage by primates and spatio-temporal variables. Olive baboons and grivet monkeys were found to target maize more frequently during June, July, and August. Olive baboons forage in the morning, while grivet monkeys do so in the afternoon. The average maize yield losses due to primate damage were 43.1% in protected fields and 31.4% in unprotected fields. Of the total damage, 43.1% occurred in protected fields situated 50 meters from the forest edge. Conversely, unprotected fields experienced lower rates of damage: 14.4%, 13.2%, 3.7%, and 0.1% at distances of 50 m, 100 m, 200 m, and 300 m from the forest edge, respectively. Camera traps captured 47 photos of baboons, 21 photos of grivet monkeys, and documented eight primate crop-foraging events. This study revealed that maize fields within 50 meters of the forest edge faced significant damage. Despite the use of wire mesh fencing, it was largely ineffective in deterring olive baboons and grivet monkeys. Additionally, while human guarding is often considered an effective protective strategy, these findings suggest its ineffectiveness due to inconsistent implementation. Overall, this study provides valuable insights for promoting primate conservation and mitigating human-primate conflicts., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Deneke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Herbicide use is widespread in agricultural production to control weeds prior to and after planting and to "burndown" weeds in the spring for conservation tillage. Whether conservation tillage adoption leads to higher herbicide usage has been a question of policy relevance for decades in the United States. Older U.S. studies using standard statistical and economic techniques have not consistently demonstrated higher herbicide usage levels among producers practicing conservation tillage, but these studies did not fully account for other practices, economic, or agronomic factors. To provide a more timely and comprehensive understanding of the importance of herbicides to conservation tillage, this study achieves two objectives with the most recent, nationally representative data from the US Department of Agriculture. First, it describes trends and compares conservation tillage and herbicide usage among field corn and soybean producers, similar to previous studies using standard economic techniques. Second, a Classification and Regression Tree (CART) model is employed-a novel methodology relative to previous studies that offers distinct advantages over traditional regression modeling-to evaluate the importance of herbicide use for conservation tillage adoption while accounting for other factors. Pairwise mean comparisons for field corn and soybeans indicated that herbicide usage pre-emergence was significantly higher with conservation tillage, but there was no consistent, significant differences in herbicide usage post-emergence. The CART analysis (with prediction accuracy ranging from 68-72%) also showed that pre-emergent use of glyphosate was the strongest predictor (with predicted probabilities from 0.83-0.86) of conservation tillage for field corn in 2016 and soybeans in 2018. Other factors such as the use of crop rotations, highly erodible land, region, and farm size were also strong predictors of conservation tillage. These findings highlight the importance and complexity of herbicide use in the adoption of conservation tillage for U.S. field corn and soybeans., Competing Interests: Authors report no competing interests., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

The sunflower crop is one of the most pro sources of vegetable oil globally. It is cultivated all around the world including Haryana, in India. However, its mapping is limited due to the requirement of huge computation power, large data storage capacity, small farm holdings, and information gap on appropriate algorithms and spectral band combinations. Thus, the current work has been done to identify an appropriate machine learning (ML) algorithm (after comparing random forest (RF) and support vector machine (SVM) reported as the best classifiers for land use and land cover) and best band combinations (among the six combinations (including Sentinel-Optical, Sentinel-SAR, and combined-Optical-SAR in single data and time series manner) for Sunflower crop mapping in Ambala and Kurukshetra districts of Haryana using Google Earth Engine (GEE) cloud platform. GEE cloud-computing system combined with RF and SVM provided Sunflower map with an accuracy ranging from 0.0% to 90% in various bands and classifiers combinations but was the highest for the RF with single date optical data. The SVM classifier tuned with parameters like kernel type, degree, gamma, and cost provided better overall accuracy for the classification of land use and land cover along with Sunflower ranging from 98.09% to 98.44% and Kappa coefficient ranging from 0.96 to 0.97 for optical data and combination of SAR and optical time series. The platform is efficient and applicable for a larger part of the country to map Sunflower and other crops with currently identified combinations of satellite data and methodology due to the availability of satellite images, advanced ML algorithms, and analytical modules on a single platform., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool. Six land use and land cover classes, viz., forests and trees outside forests, rubber plantation, shifting cultivation, water bodies, agriculture, and settlements, were mapped with an accuracy of 83.08% using a random forest classifier. This classification enabled the identification of the effective study area, which included forest areas with shifting cultivation patches. The analysis revealed that 2533.96 km 2 of the study area remained undisturbed, 568.43 km 2 experienced low-magnitude disturbance, 1501.11 km 2 were moderately disturbed, and 184.82 km 2 were highly disturbed. The shifting cultivation cycles in the study area show considerable variation. Low-magnitude disturbance indicates a single slash-and-burn event in the past three decades, moderate-magnitude disturbance involves fallow periods of over 10 years, and high-magnitude disturbance occurs with fallow periods of less than 8 years. The study revealed a shift from traditional shifting cultivation to more permanent agricultural practices in parts of Tripura. In response to incentives for commercial crops, cultivators are increasingly adopting long-term cultivation, including the growing of pineapple, areca nut, and rubber, as well as intercropping with papaya, banana, lemon, tapioca, pepper, and ginger. These plots are, however, abandoned if the yields become economically unviable. The expansion of monoculture cultivation significantly reduces the available area for shifting cultivation, thereby compelling jhum cultivators to revisit their jhum patches more frequently. This study is a first-of-its-kind attempt in the Indian context and sheds light on changing patterns in an age-old agricultural practice., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

At the dawn of new millennium, policy makers and researchers focused on sustainable agricultural growth, aiming for food security and enhanced food quality. Several emerging scientific innovations hold the promise to meet the future challenges. Nanotechnology presents a promising avenue to tackle the diverse challenges in agriculture. By leveraging nanomaterials, including nano fertilizers, pesticides, and sensors, it provides targeted delivery methods, enhancing efficacy in both crop production and protection. This integration of nanotechnology with agriculture introduces innovations like disease diagnostics, improved nutrient uptake in plants, and advanced delivery systems for agrochemicals. These precision-based approaches not only optimize resource utilization but also reduce environmental impact, aligning well with sustainability objectives. Concurrently, genetic innovations, including genome editing and advanced breeding techniques, enable the development of crops with improved yield, resilience, and nutritional content. The emergence of precision gene-editing technologies, exemplified by CRISPR/Cas9, can transform the realm of genetic modification and enabled precise manipulation of plant genomes while avoiding the incorporation of external DNAs. Integration of nanotechnology and genetic innovations in agriculture presents a transformative approach. Leveraging nanoparticles for targeted genetic modifications, nanosensors for early plant health monitoring, and precision nanomaterials for controlled delivery of inputs offers a sustainable pathway towards enhanced crop productivity, resource efficiency, and food safety throughout the agricultural lifecycle. This comprehensive review outlines the pivotal role of nanotechnology in precision agriculture, emphasizing soil health improvement, stress resilience against biotic and abiotic factors, environmental sustainability, and genetic engineering., Competing Interests: Declarations Consent for publication Not applicable. Conflict of interest The authors declare no competing interests. Consent to participate Not applicable. Ethics approval Not applicable., (© 2024. The Author(s).)

This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervasive and destructive disease that significantly impacts wheat production worldwide. Traditional diagnostic methods often require expert knowledge and time-consuming processes, making rapid and accurate detection challenging. We drafted a new, WheatRustDL2024 dataset (7998 images of healthy and infected leaves) specifically designed for VQA in the context of wheat rust detection and utilized it to retrieve the initial weights on the federated learning server. This dataset comprises high-resolution images of wheat plants, annotated with detailed questions and answers pertaining to the presence, type, and severity of rust infections. Our dataset also contains images collected from various sources and successfully highlights a wide range of conditions (different lighting, obstructions in the image, etc.) in which a wheat image may be taken, therefore making a generalized universally applicable model. The trained model was federated using Flower. Following extensive analysis, the chosen central model was ResNet. Our fine-tuned ResNet achieved an accuracy of 97.69% on the existing data. We also implemented the BLIP (Bootstrapping Language-Image Pre-training) methods that enable the model to understand complex visual and textual inputs, thereby improving the accuracy and relevance of the generated answers. The dual attention mechanism, combined with BLIP techniques, allows the model to simultaneously focus on relevant image regions and pertinent parts of the questions. We also created a custom dataset (WheatRustVQA) with our augmented dataset containing 1800 augmented images and their associated question-answer pairs. The model fetches an answer with an average BLEU score of 0.6235 on our testing partition of the dataset. This federated model is lightweight and can be seamlessly integrated into mobile phones, drones, etc. without any hardware requirement. Our results indicate that integrating deep learning with VQA for agricultural disease diagnostics not only accelerates the detection process but also reduces dependency on human experts, making it a valuable tool for farmers and agricultural professionals. This approach holds promise for broader applications in plant pathology and precision agriculture and can consequently address food security issues., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The expanding global population and the use of conventional agrochemical pesticides have led to the loss of crop yield and food shortages. Excessive pesticide used in agriculture risks life forms by contaminating soil and water resources, necessitating the use of nano agrochemicals. This article focuses on synthesis moiety and use of nanopesticides for enhanced stability, controlled release mechanisms, improved efficacy, and reduced pesticide residue levels. The current literature survey offered regulatory frameworks for commercial deployment of nanopesticides and evaluated societal and environmental impacts. Various physicochemical and biological processes, especially microorganisms and advanced oxidation techniques are important in treating pesticide residues through degradation mechanisms. Agricultural waste could be converted into nanofibers for sustainable composites production, new nanocatalysts, such as N-doped TiO 2 and bimetallic nanoparticles for advancing pesticide degradation. Microbial and enzyme methods have been listed as emerging nanobiotechnology tools in achieving a significant reduction of chlorpyrifos and dimethomorph for the management of pesticide residues in agriculture. Moreover, cutting-edge biotechnological alternatives to conventional pesticides are advocated for promoting a transition towards more sustainable pest control methodologies. Application of nanopesticides could be critical in addressing environmental concern due to its increased mobility, prolonged persistence and ecosystem toxicity. Green synthesis of nanopesticides offers solutions to environmental risks associated and using genetic engineering techniques may induce pest and disease resistance for agricultural sustainability. Production of nanopesticides from biological sources is necessary to develop and implement comprehensive strategies to uphold agricultural productivity while safeguarding environmental integrity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

As rice has no physiological capacity of fixing nitrogen in the soil, its production had always been reliant on the external application of nitrogen (N) to ensure enhanced productivity. In the light of improving nitrogen use efficiency (NUE) in rice, several advanced agronomic strategies have been proposed. However, the soared increase of the prices of N fertilizers and subsequent environmental downfalls caused by the excessive use of N fertilizers, reinforces the prerequisite adaptation of other sustainable, affordable, and globally acceptable strategies. An appropriate alternative approach would be to develop rice cultivars with better NUE. Conventional breeding techniques, however, have had only sporadic success in improving NUE, and hence, this paper proposes a new schema that employs the wholesome benefits of the recent advancements in omics technologies. The suggested approach promotes multidisciplinary research, since such cooperation enables the synthesis of many viewpoints, approaches, and data that result in a comprehensive understanding of NUE in rice. Such collaboration also encourages innovation that leads to developing rice varieties that use nitrogen more effectively, facilitate smart technology transfer, and promotes the adoption of NUE practices by farmers and stakeholders to minimize ecological impact and contribute to a sustainable agricultural future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Climate warming has become a global issue of close concern, and China, as a significant agricultural country, has an increasing demand for food, which requires China to increase carbon reduction in this industry. This paper accounts for carbon emissions from the food production industry (CEFI) using the input-output method, then screens the influencing factors of CEFI based on Random Forest (RF), analyzes the heterogeneous effects of the influencing factors on CEFI in different clusters through K-means-SHAP, and finally explores the potential of carbon emissions from this industry for the period 2024-2040. The study's findings are as follows: First, there are apparent inequalities in CEFI, especially between provinces, which are gradually increasing. Second, addressing people's consumption awareness and behaviors is not the fundamental solution to alleviate CEFI; instead, it should focus on sustainable agricultural production transformation and "food miles" in the transportation phase. In addition, attention needs to be paid to the impacts of fertilizer application, transport modes, and livestock management on the CEFI of each cluster. Finally, the study suggests that around 2028, 70% of China's provinces will be at the "carbon peak" and that less developed and more developed regions have more significant potential to reduce emissions. In this regard, this paper encourages a series of policies that are key to promoting the sustainable development of CEFI, such as reducing the volume and efficiency of traditional fertilizers, vigorously developing organic fertilizer inputs, strengthening technological innovation and R&D inputs in the transportation sector, and steadily supporting germplasm innovation in the livestock sector., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Space agriculture, pivotal for sustainable extraterrestrial missions, requires plants that can adapt to altered gravitational conditions. This study delves into the adaptive responses to altered gravity of Wolffia globosa, an aquatic plant known for its rapid growth and high nutritional value. The research aimed to analyse the effect of simulated microgravity and hypergravity on relative growth rate (RGR), morphological characteristics, protein content, and the correlation between plant size and growth rate of Wolffia globosa. The study highlighted the responses of the species to altered gravity, uncovering inherent variability among seven different clones of W. globosa. Results show a base variability among clones in terms of RGR, size and protein content. Furthermore, some clones are affected by simulated microgravity, showing a decrease in RGR. Differently, under hypergravity, clones showed RGR higher than in 1 g control, therefore revealing a novel plant response to hypergravity. Morphological adaptations to gravity alterations were also evident. Among the studied clones, significant morphological changes were observed, further underlining the peculiar adaptation to the hypergravity environment. Differently, under simulated microgravity, morphology was generally stable across clones. A key finding of the study was the significant negative correlation between RGR and the physical dimensions of the plants: the fastest growth was associated with the smallest dimensions of the plants. This correlation might have practical implications in selecting clones for space cultivation, that leads to compact yet highly productive clones. The analysis of the protein content of all the clones revealed mostly no significant changes under hypergravity. Otherwise, a general decrease in protein content was observed under simulated microgravity. Overall, the study confirms the suitability of W. globosa for space agriculture and provides new insights into the perspective of using W. globosa as an alternative crop species for protein production for manned Space missions. Furthermore, it underscores the need for focusing on the clones and the selection of the W. globosa plants that are best adapted to the environmental conditions of space; therefore, selecting those with the best combination of biomass production (by means of growth rate, size), and protein content., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background and Aims Cotton-peanut rotation is a sustainable farming practice that enhances land utilization and promotes the sustainable development of agriculture. Crop rotation can reduce the occurrence of pests and diseases, as different crops have varying levels of resistance to such threats. Additionally, by alternating the types of crops grown, the soil environment is changed, which can lead to the elimination of favorable conditions for pathogens and pests, thereby alleviating the impact of these issues. Furthermore, cotton-peanut rotation can improve soil fertility.To investigate the effects of different crop rotation systems on crop yield, soil nutrients, and soil microbial communities., Methods: Using high-throughput sequencing technology, investigate the soil microbial diversity in the root zone after cotton-peanut rotation.Various planting patterns, including cotton continuous cropping (MC), peanut continuous cropping (HC), peanut-cotton-peanut rotation (HR), and fallow (X), were established to assess variations in crop yield, soil nutrients, and soil microbial diversity., Results: Significant differences were observed in crop yield, soil nutrients, and soil microbial community structure among different planting patterns. The HR system significantly increased the output compared with the HC and MC systems. Additionally, HR exhibited significantly lower total nitrogen (N) and basic nitrogen (BN) contents than HC and MC, whereas MC showed lower total potassium (K) and available potassium (AK) contents. HR led to a decrease in soil bacterial diversity but an increase in fungal diversity, with Ascomycota and Mortierellomycota being dominant. Various bacteria (Chloroflexi, Bacteroidota, and Actinobacteriota) associated with organic matter degradation and nutrient cycling were found across different planting systems, enhancing material cycling efficiency. Furthermore, Planctomycetota bacteria related to crop nutrient synthesis and Glomeromycota bacteria aiding plant nutrient absorption were significantly higher in the MC system than in the HR or HC systems. Redundancy analysis indicated a significant negative correlation between crop rotation and soil fungal community, whereas Ascomycota exhibited a significant negative correlation with organic matter., Conclusion: Peanut-cotton rotation can mitigate soil nutrient loss, enhance beneficial microorganism diversity, suppress harmful bacterial populations, stabilize ecosystems, and boost crop yield., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Rice serves as a vital staple food, but its accumulation of cadmium (Cd) has sparked widespread concerns regarding food safety and ecosystem security. Here, we conducted a seven-year systematic field experiment in the Xiangjiang River Basin of China, where an integrated governance framework (IGF) was established to ensure rice safety. The IGF, tailored to geographical zoning and pollution gradation, includes targeted soil treatments, crop management strategies, and stakeholder engagement. The quality of both the soil and the crop was improved, with a reduction in soil Cd availability of 36%, and a decrease in Cd in rice grain of 57-78%. This framework not only addresses multiple challenges but also supports sustainable development goals (SDGs 2, 3, 6, 9) by fostering comprehensive synergies among science, policy, and local community participation. Our findings provide empirical guidance for safe rice production in Cd-contaminated areas and provide solid scientific-driven decision support globally., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The lower Chao Phraya River Basin (CPRB) in Thailand, a major rice-producing area, is grappling with increased water scarcity alongside more frequent floods and droughts, necessitating effective adaptation strategies to sustain agricultural productivity. This study assesses the impacts of climate change on rice yield and irrigation water use, using the DSSAT-CERES-Rice model. Based on these findings, potential genotype- and management-based adaptation strategies were recommended. The model was calibrated and evaluated using the data from field experiments conducted at the Asian Institute of Technology, Thailand during 2017-2018 and 2021-2022. The grain yield and irrigation water use between baseline (2010-2022) and future climate periods (early-century: 2023-2040, mid-century: 2041-2070, and late-century: 2071-2100) were compared. Future climate projections were based on five Global Climate Models (GCMs) from the NEX-GDDP-CMIP6 project under three scenarios (SSP126, SSP245, and SSP585). The model calibration and evaluation demonstrated very good performance statistics, with a d-index of 0.85 during both calibration and evaluation. The model simulations indicated that the maximum and minimum temperatures in the lower CPRB are projected to increase by ~ 2 °C and ~ 4 °C in the late century under SSP245 and SSP585, respectively. Consequently, rice yields are projected to decline by up to 33%, and irrigation water usage to increase by 53% under SSP585 by the late century. Based on the findings, the following major genotype- and management-based adaptation strategies are recommended: (1) Developing heat-tolerant rice cultivars to mitigate yield losses under future climate scenarios, (2) Developing rice cultivars with extended grain-filling duration to enhance both irrigation water use and yield, (3) Shifting the planting date 1-2 weeks earlier (from baseline planting date of 20 July), and shifting fertilizer application date 1-2 weeks earlier (from baseline fertilizer application date of 20 September) for the panicle initiation stage to improve yield, and (4) Optimizing irrigation thresholds (remaining soil water at which to irrigate) to reduce irrigation water use without compromising yield. Overall, the findings highlight the importance of genotype improvement and adaptive management practices in mitigating the adverse effects of climate change on rice production in the lower CPRB., Competing Interests: Declarations Ethical approval All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Consent to participate The authors consent to participate in this research. Consent for publication The authors consent to the publication of this research work. Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study is the first attempt to assess rice cultivation under alternate wetting and drying (AWD) and continuous flooding (CF) using the latest scenarios from the Intergovernmental Panel on Climate Change (IPCC), utilizing AquaCrop Model. Field experiments were conducted during the dry season 2023 to get the model calibration and validation input. We used two shared socioeconomic pathways scenarios (SSP3-7.0 and SSP5-8.5) developed within Coupled Model Intercomparison Project Phase 6 (CMIP6) and projected the rice growth during 2040-2070. The simulation results demonstrated the effectiveness of AquaCrop in capturing crop development across treatments and varieties. This model's accuracy in simulating canopy cover (nRMSE = 14-32.5%), time series biomass (nRMSE = 22-42.5%), grain yield (Pd = 4.36-24.38%), and total biomass (nRMSE = 0.39-18.98%) was generally acceptable. The analysis of future climate shows an increasing trend in the monthly average temperature by 0.8 °C (Tmin) and 1.3 °C (Tmax) in both scenarios. While ETo values were not anticipated, rainfall was expected to increase with average values of 5.62 mm to 11.25 mm. In addition, the study found that varieties with growing periods longer than 93 days after transplanting (DAT), such as CAR15 and Sen Kra Ob, were most impacted by heat stress conditions, leading to reduced yield, harvest index (HI), and water use efficiency (WUE). In our case, CAR15 and Sen Kra Ob grain yields were reduced by 53% and 8%, respectively. AWD maintains superior WUE compared with CF regardless of the type of varieties, suggesting this technique is a drought-adaptive strategy., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Nanotechnology represents a burgeoning field of science that enables advanced research across various domains. In recent years, there has been a notable increase in the utilization of silver nanoparticles (AgNPs) for diverse agricultural and industrial applications. Similarly, copper nanoparticles (CuNPs) have significant attention in agriculture due to their cost-effectiveness and practicality. This study aimed to investigate the potential impact of silver and copper nanoparticles on enhancing both the yield and quality of Brassica napus L. cultivar CON-1. The combined foliar application of green synthesized silver and copper nanoparticles at varying concentrations (0, 5, 10, 15, 20, 25, 30, and 35 mg/L) resulted in notable improvements in the growth, yield and quality of Brassica napus L. compared to untreated plants. Particularly, the concentration of 25 mg/L for both silver and copper nanoparticles demonstrated superior outcomes in terms of plant height, number of primary and secondary branches/plant, number of siliques /plant, number of seeds/silique, siliqua length, 1000-seed weight, biological yield/plant, seed yield/plant, oil yield/plant, and harvest index. Furthermore, the nanoparticles effectively reduced the accumulation of erucic acid in the oil, thereby enhancing the overall oil quality of canola. In summary, the application of silver and copper nanoparticles shows promising potential in improving the productivity and quality of Brassica napus L., highlighting their role as beneficial agricultural additives in modern farming practices., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

To verify an effective approach for alleviating problems associated with the consecutive monoculture of sweet potato, five fertilizer treatments were designed under plastic film-mulched, drip-fertigated conditions in fields continuously planted with sweet potato over five years. These treatments included: (1) no fertilizer application, (2) basic application of water-soluble (WS) fertilizer, (3) basic application of biofertilizer (BF) and WS fertilizer, (4) split application of WS fertilizer, and (5) combined basic application of BF and split application of WS fertilizer. The effects of BF and WS fertilizer applications on yield, quality, and soil properties were evaluated. Fertilizer applications had positive effects on increasing soil activities and nutrients compared with no fertilizer application. Split fertigation with WS fertilizer increased the activities of soil dehydrogenase (DHA) by 10.94 ~ 14.74%, alkaline phosphatase (ALP) by 4.97 ~ 5.13%, and soil organic matter (SOM) by 10.43 ~ 12.47% in the second year compared with a single application. In both years, split fertigation exerted positive effects on increasing dry matter (DM) accumulation in tuberous roots and increased the productive efficiency in nitrogen (N), phosphorus (P), and potassium (K) fertilizers. BF application for two consecutive years increased the activities of soil sucrase by 7.05 ~ 17.83%, DHA by 18.65 ~ 21.34%, ALP by 6.87 ~ 7.03%, soil available P by 18.34 ~ 28.10%, and SOM by 8.18 ~ 10.17% compared with the no BF application. BF also increased the root yield by 8.88 ~ 14.14%, the carotenoid content in tuberous roots by 20.38 ~ 30.64%, and the K utilization efficiency by 11.09 ~ 14.97%. The combination of BF and split fertigation for two consecutive years was most conducive to the activation of soil nutrients, maintenance of soil fertility, and improvement in yield and quality, which could mitigate problems associated with the consecutive monoculture of sweet potato., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethics approval Experimental research and field studies on plants, including the collection of plant material, was carried out in accordance with relevant institutional, national, and international guidelines and legislation., (© 2024. The Author(s).)

Background: An importance of tillage, inorganic and organic nutrient sources in the mustard - soybean cropping system lies in their ability to enhance soil fertility, improve nutrient availability, optimize crop growth and yield, and promote sustainable agricultural practices. Any cropping system's sustainability could be increased by implementing better management techniques like zero and reduced tillage with residue retention and better nutrient sources., Results: A field experiment was conducted for four consecutive seasons (Winter 2019 to Rainy 2021) to compare the two enhanced management practices, zero and reduced tillage to conventional tillage across four levels of nutrient sources: 75 and 100% recommended dose of nitrogen through FYM and 75 and 100% recommended dose of fertilizers through urea, single super phosphate and muriate of potash in a two years experiment. Experiment results were evaluated in terms of nutrient status, profitability and productivity of Mustard-Soybean cropping system. The results show a substantial improvement in yield, nutrient status, and overall yield performance of mustard and soybean when using the full recommended dose of fertilizers combined with reduced tillage and mulching, compared to other treatment methods. The implementation of reduced tillage practices recorded significantly higher yield of mustard and soybean over conventional and zero tillage., Conclusion: System productivity and profitability i.e. mustard equivalent yield, productivity, gross returns, net returns, profitability and B: C (benefit: cost) was found to be improved with reduced tillage and 100% recommended dose of fertilizer under reduced tillage practices. To enhance cropping system productivity in various sub-humid regions worldwide, farmers can adopt reduced tillage techniques combined with the full recommended dose of fertilizers (100% RDF)., Competing Interests: Declarations Ethical approval The experiments did not involve endangered or protected species. The plant materials in this study were used after getting the permission from Head Department of Agronomy CSK HPKV, Palampur, India. The experimental research on plants carried out in this work complies with institutional, national, and international guidelines. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Sustainable alternative farming systems are gaining popularity worldwide because of the negative effects of conventional agriculture on global climate change and the environmental degradation caused by intensive use of synthetic inputs. The green farming system in China is an integrated production strategy that focuses on reducing chemical fertilizer use while increasing organic manure inputs. Despite their rapid growth as more sustainable systems over the past decades, green farming systems have not been systematically evaluated to date. We used apple production as a representative case to assess the sustainability of green farming systems. Across major apple-producing regions in China, green farming reduced the application of chemical fertilizer nitrogen (N) by 46.8% (from 412 to 219 kg ha -1 ) and increased that of manure N by 33.1% (from 171 to 227 kg ha -1 ) on average compared with conventional systems enhancing N use efficiency by 7.27-20.27% and reducing N losses by 8.92%-11.56%. It also slightly lowered yield by 4.34%-13.8% in four provinces. Soil fertility was improved in green orchards through increases in soil organic matter, total N, and available major nutrients. Our cradle-to-farm-gate life-cycle assessment revealed that green farming helped to mitigate greenhouse gas emissions by an average of 12.6%, potentially contributing to a reduction of 165 239 t CO 2 eq annually in major apple-producing areas. In addition, green farming achieved 39.3% higher profitability ($7180 ha -1 year -1 ) at the farmer level. Our study demonstrates the potential of green production of apples for the development of sustainable agriculture in China. These findings advance our understanding of sustainable alternative farming systems and offer perspectives for the sustainable development of global agriculture., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Maize is one of the important cereal crops grown in rainfed regions of northwestern Himalayas, however, persistent use of chemical fertilizers coupled with poor soil nutrients and water holding capacity due to coarse textured soils poses serious threat to sustaining maize yield and soil health. To address these bottlenecks, a long-term experiment with application of organic manures and mineral fertilizer provides insights to quantify changes in soil organic carbon (SOC), crop yield and rain water use efficiency (RWUE) in rainfed area having low water use efficiency. A twelve years field experiment was conducted under dry sub-humid Inceptisols in northern India to study the potential impacts of organic and mineral fertilization on maize (Zea mays L.) productivity, water use efficiency and soil quality. Ten treatments were assessed, involving different nitrogen levels (20, 30, and 40 kg N ha⁻¹) combined with 10 tha⁻¹ year⁻¹ of farmyard manure (FYM), in-situ green manure from sunhemp, and the incorporation of Leucaena leucocephala leaves at 5 tha⁻¹ year⁻¹, including an unfertilized control. Maize yield increased linearly with increasing nitrogen application rates. The combination of FYM @ 10t ha -1 and 40 kg N ha -1 (T4) yielded the highest maize production. Manure addition improved soil organic carbon (SOC) and major soil nutrients (N, P and K) while unfertilized control showed decline in soil nutrients compared to their initial values. Compared with control, incorporation of 10 t ha -1 FYM increased SOC by 1.3, 1.41, 1.44 times at application rate of 20, 30, 40 kg N ha -1, respectively. Application of N@40 kg ha -1 + 10t FYM ha -1 showed highest rain water use efficiency (RWUE) and relative production efficiency index (RPEI) (2.74 kg ha -1 mm -1 and 82, respectively) and the lowest rank sum of 6. Highly significant positive relationship existed between RPEI and RWUE, RPEI and sustainability yield index (SYI), RWUE and SYI indicated the superiority of FYM in combination with mineral fertilizer. Regression models, correlating yield with monthly rainfall and crop growing periods, indicated that the integration of FYM (10 tha⁻¹) with 40 kg N ha⁻¹ was most effective in achieving the highest relative soil quality index (RSQI) of 76 and the greatest sustainability yield index (SYI) of 49.3%. Based on results, we recommend balanced fertilization (N@40 kg ha -1 +10t FYM ha -1 ) which is easily manageable by farmers as the optimal strategy for improving soil quality and achieving sustainable maize productivity in nutrient depleted Inceptisols of northern India., (© 2024. The Author(s).)

Improving farmer cooperatives' operating performance is essential for their economic sustainability and for promoting sustainable agricultural development. Despite the prominent role of benefit distribution and asset specificity in cooperation organizations, their impact on performance remains controversial, and the relationship between the three is unclear. This study focuses on cooperative benefit distribution in terms of their rebates (first and second rebates) and investigates the relationship between asset specificity, cooperative rebate and operating performance. The results show that both the first and second rebates have a positive impact on operating performance and play substitutive roles in increasing operating performance. Physical asset specificity (PAS) impacts first rebates positively, whereas human asset specificity (HAS) impacts first rebates negatively. PAS has a positive effect on operating performance whereas HAS has no significant influence on operating performance. Furthermore, the first rebate mediates the relationship between PAS and operating performance and suppresses the relationship between HAS and operating performance. These findings provide support for developing relevant policies to improve cooperative performance and sustainability., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zeng, Wan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agricultural practices, such as the Ribb watershed in Ethiopia. However, MODIS sensors have recently given high temporal resolution ET products across large areas, but their low spatial resolution limits its application on a local scale. The primary goal of the study was to downscale the MODIS ET (1 km) product to a finer spatial resolution at the watershed level. The model's 12 predictor variables (NDVI, EVI, LAI, FVC, SAVI, NDMI, NDWI, Albedo, emissivity, LST, and DEM: slope and elevation) were produced using the random forest (RF) algorithm using Sentinel-2 (S-2) 20 m and Landsat-8 (L-8) 30 m. The RF regression model was used to assess the relationship between predicted variables and downscaled MODIS ET. The FAO-PM ET model, developed from meteorological stations, was validated by R 2 and RMSE for three seasons (rainy, post-rainy, and dry) in 2022. The results were in good agreement with MODIS ET, with an RMSE of 0.22 for S-2 and 0.28 for L-8. In the FAO-PM ET model, the downscaled result showed greater spatial details and better agreement with gage station readings ( R 2 ≈ 0.88 and 0.82 ). Thus, considering the effectiveness and simplicity of machine learning techniques, our study demonstrated the potential for ET downscaling. Furthermore, the study suggests integrating spatiotemporal time series data to reach higher resolution., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

With the increasing pressure on global food security, the effective detection and management of rice pests have become crucial. Traditional pest detection methods are not only time-consuming and labor-intensive but also often fail to achieve real-time monitoring and rapid response. This study aims to address the issue of rice pest detection through deep learning techniques to enhance agricultural productivity and sustainability. The research utilizes the IP102 large-scale rice pest benchmark dataset, publicly released by CVPR in 2019, which includes 9,663 images of eight types of pests, with a training-to-testing ratio of 8:2. By optimizing the YOLOv8 model, incorporating the CBAM (Convolutional Block Attention Module) attention mechanism, and the BiFPN (Bidirectional Feature Pyramid Network) for feature fusion, the detection accuracy in complex agricultural environments was significantly improved. Experimental results show that the improved YOLOv8 model achieved mAP@0.5 and mAP@0.5:0.95 scores of 98.8% and 78.6%, respectively, representing increases of 2.8% and 2.35% over the original model. This study confirms the potential of deep learning technology in the field of pest detection, providing a new technological approach for future agricultural pest management., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xiong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The agricultural WSN (wireless sensor network) has the characteristics of long operation cycle and wide coverage area. In order to cover as much area as possible, farms usually deploy multiple monitoring devices in different locations of the same area. Due to different types of equipment, monitoring data will vary greatly, and too many monitoring nodes also reduce the efficiency of the network. Although there have been some studies on data fusion algorithms, they have problems such as ignoring the dynamic changes of time series, weak anti-interference ability, and poor processing of data fluctuations. So in this study, a data fusion algorithm for optimal node tracking in agricultural wireless sensor networks is designed. By introducing the dynamic bending distance in the dynamic time warping algorithm to replace the absolute distance in the fuzzy association algorithm and combine the sensor's own reliability and association degree as the weighted fusion weight, which improved the fuzzy association algorithm. Finally, another three algorithm were tested for multi-temperature sensor data fusion. Compare with the kalman filter, arithmetic mean and fuzzy association algorithm, the average value of the improved data fusion algorithm is 29.5703, which is close to the average value of the other three algorithms, indicating that the data distribution is more even. Its extremely bad value is 8.9767, which is 10.04%, 1.14% and 9.85% smaller than the other three algorithms, indicating that it is more robust when dealing with outliers. Its variance is 2.6438, which is 2.82%, 0.65% and 0.27% smaller than the other three algorithms, indicating that it is more stable and has less data volatility. The results show that the algorithm proposed in this study has higher fusion accuracy and better robustness, which can obtain the fusion value that truly feedbacks the agricultural environment conditions. It reduces production costs by reducing redundant monitoring devices, the energy consumption and improves the data collection efficiency in wireless sensor networks., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ruipeng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Land is a finite resource that must be managed wisely to ensure its sustainability. Consequently, land evaluation has become essential. Identifying and utilizing productive capacity of land efficiently and profitably is crucial; otherwise, resource degradation can severely impact natural ecosystems and food production. Over the years, various methodologies have been employed to assess land resources, and one such method is the Land Capability Classification (LCC). LCC is a widely used and fundamental approach to land-use planning, traditionally assessing land based on its intrinsic qualities and climate. This study aims to highlight new approaches and developments in land evaluation techniques. It provides a brief overview of recent technological and scientific advancements integrated into land evaluation. The findings suggest that LCC alone is inadequate for precise land assessment, emphasizing the need to integrate new technologies. Remote Sensing (RS) and Geographic Information System (GIS) technologies are becoming increasingly significant. Integrating various software, decision-making systems, and mathematical models can also enhance the accuracy of land assessment results. The continuous advancement of GIS and remote sensing technologies is paving the way for new tools to facilitate natural resource mapping, appraisal, surveillance, and management. Utilizing these technologies for future projections will be highly beneficial in accurately assessing the long-term impacts of current land management practices., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Knowledge on the variation of yield and water use efficiency under different mulching methods is important for guiding rained maize production in the Loess Plateau area. In this study, eight different plastic film mulching methods was established to analyze the maize growth, soil water content and soil temperature changes of dryland maize, and increase yield and water use efficiency (WUE). The field experiment was conducted in 2019, and eight treatments were set up, including a traditional flat planting without mulching (CK), ridge-furrow with ridges mulching black plastic film and furrows mulching straw (HJ), ridge-furrow with ridges mulching black plastic film and furrows bare (HL), ridge-furrow with ridges mulching liquid plastic film and furrows mulching straw (YJ), ridge-furrow with ridges mulching liquid plastic film and furrows bare (YL), ridge-furrow with ridges mulching biodegradable plastic film and furrows mulching straw (SJ), ridge-furrow with ridges mulching biodegradable plastic film and furrows bare (SL) and ridge-furrow with ridges bare and furrows mulching straw (NJ). Furthermore, the AHP-TOPSIS was employed to evaluate the optimal mulching method for maize. The results showed that compared with CK and NJ treatment, the soil water content and soil storage were significantly changes with other treatments in the reproductive period of maize. Among the six mulching methods, maize yield in HJ, HL, YJ, YL, SJ, and SL treatments were 46.28%, 61.95%, 70.30%, 51.02%, 52.02% and 53.53% significantly greater than CK treatment. In addition, dryland maize WUE was 66.53% and 84.01% higher in the YJ and YL treatments with ridges mulching liquid plastic film than in the CK treatment, respectively. The optimal treatments of economic benefits were YL and HJ. Through AHP-TOPSIS comprehensive analysis, the optimal mulching methods were YL and HJ treatment. Current field trials indicate that YL treatment could serve as a promising option to improve dryland maize yield, WUE, and reducing environmental risks in the Loess Plateau of China., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Cassava is a staple in the diet of millions of people in sub-Saharan Africa, as it can grow in poor soils with limited inputs and can withstand a wide range of environmental conditions, including drought. Previous studies have shown that the distribution of rural populations is an important predictor of cassava density in sub-Saharan Africa's landscape. Our aim is to explore relationships between the distribution of cassava from the cassava production disaggregation models (CassavaMap and MapSPAM) and rural population density, looking at potential differences between countries and regions. We analysed various properties of cassava cultivations collected from surveys at 69 locations in Côte d'Ivoire and 87 locations in Uganda conducted between February and March 2018. The relationships between the proportion of surveyed land under cassava cultivation and rural population and settlement data were examined using a set of generalized additive models within each country. Information on rural settlements was aggregated around the survey locations at 2, 5 and 10 km circular buffers. The analysis of the original survey data showed no significant correlation between rural population and cassava production in both MapSPAM and CassavaMap. However, as we aggregate settlement buffers around the survey locations using CassavaMap, we find that at a large scale this model does capture large-scale variations in cassava production. Moreover, through our analyses, we discovered country-specific spatial trends linked to areas of higher cassava production. These analyses are useful for validating disaggregation models of cassava production. As the certainty that existing cassava production maps increases, analyses that rely on the disaggregation maps, such as models of disease spread, nutrient availability from cassava with respect to population in a region, etc. can be performed with increased confidence. These benefit social and natural scientists, policymakers and the population in general by ensuring that cassava production estimates are increasingly reliable., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hassall et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Wheat (Triticum aestivum L.) is a staple crop of immense economic importance, especially in the agronomic context of Dera Ismail Khan, Khyber Pakhtunkhwa (KPK), Pakistan. Understanding the effects of various agronomic practices, such as priming techniques, sowing methods, and seed rates, on wheat yield and growth is crucial for optimizing production and ensuring regional food security. To study the effects of priming and sowing techniques in combination with seed rates on short (Israr Shaheed) and long (Gomal) duration, three experiments were conducted at the agronomic research site of Gomal University, Dera Ismail Khan, Pakistan, wheat varieties. In the first experiment, various priming techniques were used. Among different techniques used Farmyard manure priming produced the tallest plants at 111.3 and 125.3 cm, the maximum number of tillers (335.7 and 345.0 m -2 ), longest spikes (8.6 and 7.6 cm), maximum spikelets (27.2 and 31.0 spike -1 ), maximum number of grains (45.5 and 48.7 spike -1 ), maximum grain yield (4.4 and 4.6 t ha -1 ) and maximum harvest index (38.7 and 41.5%) respectively for Israr Shaheed and Gomal. Conversely, compost manure produced heavier grains than other interactions (49.2 and 42.3 g) while sodium chloride showed the maximum biological yield (12.4 and 11.8 t ha -1 ) for Israr Shaheed and Gomal correspondingly. In 2nd experiment, different sowing methods were tested on two wheat varieties Israr Shaheed and Gomal. Dry sowing followed by flooding produced tallest plants measuring (105.5 and 101.7 cm), maximum number of tillers (398.2 and 406.7 m -2 ), longest spikes (8.3 and 7.2 cm), maximum spikelets (29.8 and 35.5 spike -1 ), maximum number of grains (54.3 and 51.6 spike -1 ) and maximum grain yield (4.3 and 4.6 t ha -1 ) respectively for Israr Shaheed and Gomal. Israr Shaheed and Gomal showed maximum 1000-grain weights (45.3 and 45.0 g) in sprouted seeding in flooded fields respectively. The highest biological yield (13.6 t ha-1) was in dry seed in the flooded field while the maximum harvest index (33.5 and 33.9%) was recorded in soaked seed followed by flooding for Israr Shaheed and Gomal. In the last experiment, different seed rates were studied. The results showed that plants grown with seed rate 150 kg ha -1 showed the tallest plants at 111.0 and 111.7 cm, maximum number of tillers (411.6 and 408.2 m -2 ), longest spikes (7.8 and 7.7 cm), maximum spikelets (34.3 and 34.3 spike -1 ), maximum number of grains (50.8 and 47.2 spike -1 ), maximum grain yield (4.6 and 4.5 t ha -1 ) and maximum harvest index (38.4 and 36.7%) respectively for Israr Shaheed and Gomal. The maximum number of 1000-grains weight (g) was found on 200 and 250 kg ha -1 for Israr Shaheed and Goma-8, respectively giving 45.2 g while biological yield was the highest in 175 kg ha -1 respectively. Conclusively, the results indicated that yield and yield attributes in wheat were enhanced up to 150 kg ha -1 seed rate when interacted with farmyard manure priming along with dry sowing of seed followed by flooding. Future research should explore the integration of advanced irrigation techniques and nutrient management practices to further enhance wheat productivity in the Dera Ismail Khan region., (© 2024. The Author(s).)

Watershed characterization is essential for sustainable watershed management and effective resource utilization, particularly in assessing changes resulting from interventions. This study investigates the biophysical and socio-economic conditions of the Agewmariam watershed, aiming to develop land capability and suitability maps while identifying viable management options. Biophysical data, including land slope, soil properties, erosion severity, stoniness/rockiness cover, and vegetation cover, were collected through field surveys and analyzed using overlay analysis in ArcGIS. Socio-economic data, encompassing population demographics, income, and expenditure, were gathered via individual interviews, complete census, and focus group discussions and summarized using descriptive statistics. The study identified 259 households with an average family size of 4 and a demographic dependency ratio of 96.6%. The major sources of income are crop production and livestock rearing supplemented by casual labor and food aid. The average farmland holding size is 0.5 ha, with sorghum, barley, teff, and wheat as the dominant crops. The watershed is characterized by six land capability classes (classes II, III, IV, VI, VII, and VIII). The suitability analysis indicated that the watershed is not currently suitable for wheat and teff crops unless physical, chemical, and biological management improvements are implemented. The study revealed that the major limiting factors for land capability and suitability were slope, erosion severity, stoniness, soil organic matter, and soil texture. The socio-economic characterization enhanced awareness about the local socio-economic condition, informing appropriate planning and management strategies. The study recommends intensive soil and water conservation intervention, afforestation on hillsides, changing the land use system, and the addition of organic matter and fertilizers to enhance land capability and suitability for sustainable agricultural practices., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Continuous cropping of the same crop leads to land degradation. This is also called the continuous-cropping obstacle. Currently, intercropping tobacco with other crops can serve as an effective strategy to alleviate continuous cropping obstacles., Results: In this study, tobacco K326 and insectary floral plants were used as materials, and seven treatments of tobacco monoculture (CK), tobacco intercropped with Tagetes erecta, Vicia villosa, Fagopyrum esculentum, Lobularia maritima, Trifolium repens, and Argyranthemum frutescens respectively, were set up to study their effects on rhizosphere soil chemical properties and composition and structure of rhizosphere soil microbial community of tobacco. The 16 S rRNA gene and ITS amplicons were sequenced using Illumina high-throughput sequencing. tobacco/insectary floral plants intercropping can influence rhizosphere soil chemical properties, which also change rhizosphere microbial communities. The CK and treatment groups tobacco rhizosphere soil microorganisms had significantly different genera, such as tobacco intercropping with T. repens and A. frutescens significantly increased the number of Fusarium and intercropping T. erecta, V. villosa, L. maritima, T. repens, and A. frutescens significantly increased the number of Sphingomonas and unknown Gemmatimonadaceae. Additionally, intercropping T. erecta, V. villosa and L. maritima changed the rhizosphere fungal and bacteria community and composition of tobacco and the positive correlation between tobacco rhizosphere the genera of fungi and bacterial were greater than CK. The pathway of the carbohydrate metabolism, amino acid metabolism, and energy metabolism in rhizosphere bacteria were significantly decreased after continuous cropping. Fungal symbiotic trophic and saprophytic trophic were significantly increased after intercropping V. villosa, L. maritima and plant pathogen and animal pathogen were increased after intercropping T. repens and A. frutescens. Additionally, bacterial and fungal communities significantly correlated with soil chemical properties, respectively., Conclusion: This study reveals that intercropping tobacco with insectary floral plants, particularly T. erecta, V. villosa, L. maritima and A. frutescens significantly affects soil chemical properties and alters rhizosphere microbial communities, increasing the abundance of certain microbial genera. Additionally, intercropping enhances pathways related to carbohydrate, amino acid, and energy metabolism in rhizosphere bacteria. These findings suggest that intercropping could provide a promising strategy to overcome challenges associated with continuous tobacco cropping by regulating the rhizosphere environment., (© 2024. The Author(s).)

" O fortunatos nimium, sua si bona norint agricolas! " [...].

Hyperspectral imaging provides high-dimensional spatial-temporal-spectral information showing intrinsic matter characteristics 1-5 . Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible-near-infrared (400-1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation., (© 2024. The Author(s).)

Over half the global population depends on food grown with synthetic nitrogen fertilizers, but much of this nitrogen is lost as nitrates, N 2 O and NH 3 , harming the environment and health and incurring substantial environmental costs. Practical technologies aimed at enhancing nitrogen efficiency to reduce these losses promised considerable societal benefits. Nitrification inhibitors (NIs) can help reduce these losses, but their effectiveness varies, often lasting only weeks or days, for the strategy to improve NIs efficiency reducing environmental pollution that are still poorly contrived. Therefore, this study developed a novel approach by ecofriendly alginate and polyphenols to create a microenvironment (SANMP), which increased NIs based on DMPP stability at temperatures between 70 and 125 °C (47%-77% increase), in compound fertilizers (1.4%-11% increase), and in soils with a wide pH range of 5.6-7.9 (21%-27% increase). Enhanced stability can significantly increase environmental benefits in agriculture. SANMP reduces N 2 O emissions by 89% relative to nitrogen fertilizer-only treatments and a further 26% decrease compared to traditional DMPP formulations. Analysis of the chemical structure of alginate-metal-polyphenol hybrid materials demonstrated that DMPP immobilization, achieved through pore filling, chelation, and electrostatic attraction, significantly reduced its degradation from high temperatures, pH fluctuations, environmental ions, and soil microbial activities. The novel microenvironment offers an effective solution to the problems of high cost and unstable inhibition efficiency of DMPP, thus improving its environmental and agricultural benefits. This technology promises to offer solutions for nutrient management strategies that are efficient, highly beneficial to the environment and cost-effective., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Rui Liu reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity., Results: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses., Conclusions: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments., (© 2024. The Author(s).)

Brazil is one of the world's largest beer producers and also a major food producer. These activities generate a large amount of residues which, if disposed of inappropriately, can have adverse effects on the environment. The objective of this research was to evaluate the potential of using these residues for both mushroom cultivation (traditional use) and the production of mycelium-based composites (innovative use). Mushroom production (Pleurotus sajor-caju) was conducted using only brewer's spent grains (fresh and dried) and also mixed with banana leaves (1:1) or peach palm leaves (1:1), which are residues widely available in the northern region of Santa Catarina, Brazil. The productivity of mushrooms cultivated using fresh and dried brewer's spent grains did not exhibit a statistically significant difference, indicating that this residue can be utilized shortly after its generation in the industrial process, thereby reducing costs associated with production. Combining brewer's spent grains with banana or peach palm leaves resulted in enhanced mushroom production (0.41 and 0.38 g day -1 , respectively) compared to using the leaves as a sole substrate. The mushrooms produced contain sugars and a minimal sodium content, and are considered a source of phosphorus. In addition, no toxic elements (Hg and Pb) were present. The mycelium-based composites produced using the residual substrate (after the mushroom harvest) exhibited better mechanical properties (compressive strength = 0.04 MPa, density = 242 kg m -3 , and low humidity sorption) than those produced using fresh substrate. The results demonstrate the synergistic effect of combining the two approaches under investigation. The use of brewer´s spent enhance the mushroom productivity and the residual substrate enhance the mechanical properties of mycelium-based composites. The compressive strength, density, and air humidity sorption properties are essential for determining the potential applications of mycelium-based composites. The use of brewer's spent grains mixed with banana leaves demonstrated significant promise for mushroom production and subsequent application in the development of mycelium-based composites. These sequential approaches contribute to waste valorization and the rational utilization of natural resources, as the mycelium-based composites are considered for substitution of synthetic materials, thereby promoting sustainability for future generations., (© 2024. The Author(s).)

The development of multi-cropping systems is hampered by the seasonal shortage problem of growing two or three crops within a year. Exploring strategies to alleviate phenological constraints in multi-cropping systems is crucial for increasing grain production. Using a county-level panel dataset with detailed crop progress information from China, this study investigates whether mechanized winter wheat harvest can alleviate the phenological constraints of a shorter growing season for subsequent summer corn in a wheat-corn double cropping system. The results show that mechanized winter wheat harvest considerably extends the length of the summer corn growing season. This spillover effect of mechanized winter wheat harvest is particularly evident in plains and hilly areas with larger farm sizes. Further analysis of the mechanism indicates that mechanized winter wheat harvest affects the length of the summer corn growing season by advancing the planting date and delaying the maturity date. These results underscore the importance of advancing agricultural mechanization to enhance food security under limited land resources., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Soil erosion by water is a serious problem in Ethiopia, contributing to diminishing crop yields and food shortages. Apart from understanding the magnitude, risk, and spatial distribution of the problem, identifying erosion hotspot areas is essential for effectively reversing the problem. This study aims to identify erosion hotspots in the Gotu watershed, in northeastern Ethiopia, using the revised universal soil loss equation (RUSLE) and incorporating local farmers' perspectives to prioritize conservation efforts. The RUSLE model reveals that 29,744.3 metric tons of soil is lost annually from the Gotu watershed, with an average loss of 65.3 to t ha⁻ 1  year⁻ 1 . The main contributing factors to soil erosion in the watershed include undulating topography, loss of plant cover, and continuous cultivation. The highest soil loss rates (> 80 t ha⁻ 1  year⁻ 1 ) were found in the western, northern, and southern parts of the watershed, where cultivation occurs on moderate to steep slopes with sparse vegetation cover. These areas should be prioritized for conservation interventions. Farmers identified poor crop yields and damaged conservation structures as key indicators of soil erosion prevalence in the watershed. Increasing farmer's understanding of soil erosion and the importance of soil and water conservation is essential for effectively controlling soil erosion and improving food security in the area., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Nanopesticides (Npes) offer improved efficacy compared to their conventional forms while reducing the usage/application rates, hence being more sustainable options. However, there is still a knowledge gap on the Npes environmental impacts. To support the safety of nano-enabled pesticides, the present study aimed at assessing the toxicity of the commercial Npe NUCOP-M and the active substance copper oxychloride, using the ecotoxicological soil model Enchytraeus crypticus and LUFA 2.2 soil. Bioassays were performed to assess various endpoints within short-to longer-term exposures: avoidance behaviour (2 d), hatching (13 d), survival, reproduction and organisms' size (based on the standard OECD test (28 d), the OECD extension (56 d), and the Full Life Cycle test - FLCt (46 d)). Based on the standard OECD test and its extension, NUCOP-M had a similar level of toxicity as copper oxychloride without indications of increase in toxicity over time (28 versus 56 d). The shorter-term exposures (2 and 13 d) showed higher toxicity for copper oxychloride. The exposure from cocoon stage (FLCt) seemed to provide an adaptative advantage (reduced toxicity) to NUCOP-M. The differences might be related to a slower release of Cu 2+ ions from NUCOP-M, which seems to account for the toxicity at longer-term. Based on the recommended application doses (ca. 1.72 mg NUCOP-M kg -1 , i.e. 0.62 mg Cu kg -1 in the topsoil) there is no unacceptable risk of NUCOP-M on the enchytraeid population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Soil ecosystems are complex networks of microorganisms that play pivotal roles in nutrient cycling, stress resilience, and the provision of ecosystem services. Among these microbial communities, soil biofilms, and complex aggregations of microorganisms embedded within extracellular polymeric substances (EPS) exert significant influence on soil health and function. This review delves into the dynamics of soil biofilms, highlighting their structural intricacies and the mechanisms by which they facilitate nutrient cycling, and discusses how biofilms enhance the degradation of pollutants through the action of extracellular enzymes and horizontal gene transfer, contributing to soil detoxification and fertility. Furthermore, the role of soil biofilms in stress resilience is underscored, as they form symbiotic relationships with plants, bolstering their growth and resistance to environmental stressors. The review also explores the ecological functions of biofilms in enhancing soil structure stability by promoting aggregate formation, which is crucial for water retention and aeration. By integrating these insights, we aim to provide a comprehensive understanding of the multifaceted benefits of biofilms in soil ecosystems. This knowledge is essential for developing strategies to manipulate soil biofilms to improve agricultural productivity and ecological sustainability. This review also identifies research gaps and emphasizes the need for practical applications of biofilms in sustainable agriculture., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Natural habitat patches can be essential in conserving biodiversity and providing ecosystem services for agroecological systems. However, adopting a land-sharing approach requires a deep understanding of the agricultural costs and benefits associated with the existence of adjacent natural habitat patches. We used 17 paired natural habitat patch-wheat field replicates to test the effects of distance from the natural patches and removal of ground-dwelling arthropods from the field on the wheat yield. Removing ground-dwelling arthropods increased wheat yield. Community compositions of ground-dwelling arthropods in the natural habitat patches and wheat fields varied significantly, irrespective of the distance from the natural patch. In April and May, near the wheat harvest, predator abundance was higher in the natural habitat patches than in the wheat fields, whereas the abundance of potential pests was much higher in the wheat fields. This reduced predator-pest ratio in the wheat fields may explain why removing ground-dwelling arthropods increased wheat yield. Future research should focus on developing effective methods for managing ground-dwelling and vertical wheat pest populations while preserving their natural enemies' integrity within the fields and adjacent natural habitat patches., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Yaron Ziv and Guy Rotem reports financial support was provided by Nekudat Chen Fund. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Understanding the transition from meteorological to agricultural drought is crucial for developing effective drought management strategies and early warning systems. This study provides a unique perspective by utilizing hybrid drought indices to explore the temporal and spatial complexities of drought propagation across two large watersheds-California and Mississippi-that feature distinct agro-climatic conditions and irrigation practices. We assess the links between meteorological drought, measured by the Standardized Precipitation Index (SPI), and agricultural drought using three indicators: Vegetation Drought Response Index (VegDRI), GRACE Root Zone Soil Moisture Percentile (SMI), and the Evaporative Demand Drought Index (EDDI). By comparing watersheds with different irrigation systems, we also consider the role of irrigation in modifying drought dynamics. Our analysis identified the 14-day SPI as particularly effective in capturing drought dynamics. Soil moisture showed the earliest response to rainfall anomalies, with a lag of 41 days in California and 59 days in Mississippi. Vegetation stress followed, with a lag of 137 days in California and 75 days in Mississippi, while atmospheric conditions lagged by 143 and 139 days, respectively. Irrigation was found to delay drought impacts by up to two months. This framework offers a finer temporal resolution and a more nuanced understanding of drought propagation, potentially informing more targeted drought mitigation strategies than traditional methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Water scarcity is a global challenge. A severe gap between water supply and demand will arise. Consequently, a large part of the world could face water shortage issues in the near future. Increasing cultivated areas and rapid population growth will intensify water consumption, which may lead to a "drink or grow" situation in many countries. Promoting more yield per water drop (MYWD) ideology for water secure development is critically important because the agriculture sector is the largest water consumer by 70%. In lower-middle and low-income countries, water use in agriculture ranges from 80-90%. Advanced water-saving technologies can reduce water consumption by 35-65%, but adoption is less than 20% of the total irrigated area in most countries. Mission 2050 in agricultural countries would be to cover at least 75% and 85% of land under water-saving technologies, which receive surface and groundwater, respectively. The water-saving technologies can decrease farm-scale water consumption, thus alleviating pressure on water resources. In the water scarcity mitigation agenda, the increasing cultivated area under the water-saving technologies should be mapped well since some researchers believe that the water-saving technologies are increasing cultivated area, thereby jeopardizing the "water-saving goal." This comprehensive review navigated the MYWD concept, discussed strategies, sketched a hydrological model to conserve resources, and highlighted economic feasibility, environmental benefits of water-saving technologies, and further improvements. This study can contribute significantly to the future water policy and measures worldwide., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)