bluuebunny/crossref_metadata_2025 huggingface dataset:
Systems-level proteomics and metabolomics reveals the diel molecular landscape of diverse kale cultivars
Authors: Scandola Sabine , Mehta Devang , Castillo Brigo , Boyce Nicholas , Uhrig R. Glen
Published: July 2023

Kale is a group of diverse Brassicaceae species that are nutritious leafy greens consumed for their abundance of vitamins and micronutrients. Typified by their curly, serrated and/or wavy leaves, kale varieties have been primarily defined based on their leaf morphology and geographic origin, despite having complex genetic backgrounds. Kale is a very promising crop for vertical farming due to its high nutritional content; however, being a non-model organism, foundational, systems-level analyses of kale are lacking. Previous studies in kale have shown that time-of-day harvesting can affect its nutritional composition. Therefore, to gain a systems-level diel understanding of kale across its wide-ranging and diverse genetic landscape, we selected nine publicly available and commercially grown kale cultivars for growth under near-sunlight LED light conditions ideal for vertical farming. We then analyzed changes in morphology, growth and nutrition using a combination of plant phenotyping, proteomics and metabolomics. As the diel molecular activities of plants drive their daily growth and development, ultimately determining their productivity as a crop, we harvested kale leaf tissue at both end-of-day (ED) and end-of-night (EN) time-points for all molecular analyses. Our results reveal that diel proteome and metabolome signatures divide the selected kale cultivars into two groups defined by their amino acid and sugar content, along with significant proteome differences involving carbon and nitrogen metabolism, mRNA splicing, protein translation and light harvesting. Together, our multi-cultivar, multi-omic analysis provides new insights into the molecular underpinnings of the diel growth and development landscape of kale, advancing our fundamental understanding of this nutritious leafy green super-food for horticulture/vertical farming applications.
Abundance considerations for modeling yield of rapeseed at the flowering stage
Authors: Li Yuanjin , Yuan Ningge , Luo Shanjun , Yang Kaili , Fang Shenghui , Peng Yi , Gong Yan
Published: July 2023

Introduction

To stabilize the edible oil market, it is necessary to determine the oil yield in advance, so the accurate and fast technology of estimating rapeseed yield is of great significance in agricultural production activities. Due to the long flowering time of rapeseed and the characteristics of petal color that are obviously different from other crops, the flowering period can be carefully considered in crop classification and yield estimation.

Methods

A field experiment was conducted to obtain the unmanned aerial vehicle (UAV) multispectral images. Field measurements consisted of the reflectance of flowers, leaves, and soils at the flowering stage and rapeseed yield at physiological maturity. Moreover, GF-1 and Sentinel-2 satellite images were collected to compare the applicability of yield estimation methods. The abundance of different organs of rapeseed was extracted by the spectral mixture analysis (SMA) technology, which was multiplied by vegetation indices (VIs) respectively to estimate the yield.

Results

For the UAV-scale, the product of VIs and leaf abundance (AbdLF) was closely related to rapeseed yield, which was better than the VIs models for yield estimation, with the coefficient of determination (R2) above 0.78. The yield estimation models of the product of normalized difference yellowness index (NDYI), enhanced vegetation index (EVI) and AbdLF had the highest accuracy, with the coefficients of variation (CVs) below 10%. For the satellite scale, most of the estimation models of the product of VIs and rapeseed AbdLF were also improved compared with the VIs models. The yield estimation models of the product of AbdLF and renormalized difference VI (RDVI) and EVI (RDVI×AbdLF and EVI×AbdLF) had the steady improvement, with CVs below 13.1%. Furthermore, the yield estimation models of the product of AbdLF and normalized difference VI (NDVI), visible atmospherically resistant index (VARI), RDVI, and EVI had consistent performance at both UAV and satellite scales.

Discussion

The results showed that considering SMA could improve the limitation of using only VIs to retrieve rapeseed yield at the flowering stage. Our results indicate that the abundance of rapeseed leaves can be a potential indicator of yield prediction during the flowering stage.
Towards deep learning based smart farming for intelligent weeds management in crops
Authors: Saqib Muhammad Ali , Aqib Muhammad , Tahir Muhammad Naveed , Hafeez Yaser
Published: July 2023

Introduction

Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops.

Methods

Our proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny).

Results and discussion

The effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value.

Future work

In the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time.
Transcriptomic analysis in tomato fruit reveals divergences in genes involved in cold stress response and fruit ripening
Authors: Mitalo Oscar W. , Kang Seung Won , Tran Long T. , Kubo Yasutaka , Ariizumi Tohru , Ezura Hiroshi
Published: July 2023

Cold storage is widely used to extend the postharvest life of most horticultural crops, including tomatoes, but this practice triggers cold stress and leads to the development of undesirable chilling injury (CI) symptoms. The underlying mechanisms of cold stress response and CI development in fruits remain unclear as they are often intermingled with fruit ripening changes. To gain insight into cold responses in fruits, we examined the effect of the potent ethylene signaling inhibitor 1-methylcyclopropene (1-MCP) on fruit ripening, CI occurrence and gene expression in mature green tomatoes during storage at 20°C and 5°C. 1-MCP treatments effectively inhibited ethylene production and peel color changes during storage at 20°C. Storage at 5°C also inhibited both ethylene production and peel color change; during rewarming at 20°C, 1-MCP treatments inhibited peel color change but failed to inhibit ethylene production. Furthermore, fruits stored at 5°C for 14 d developed CI symptoms (surface pitting and decay) during the rewarming period at 20°C regardless of 1-MCP treatment. Subsequent RNA-Seq analysis revealed that cold stress triggers a large-scale transcriptomic adjustment, as noticeably more genes were differentially expressed at 5°C (8,406) than at 20°C (4,814). More importantly, we have found some important divergences among genes involved in fruit ripening (up- or down-regulated at 20°C; inhibited by 1-MCP treatment) and those involved in cold stress (up- or down-regulated at 5°C; unaffected by 1-MCP treatment). Transcriptomic adjustments unique to cold stress response were associated with ribosome biogenesis, NcRNA metabolism, DNA methylation, chromatin formation/remodeling, and alternative splicing events. These data should foster further research into cold stress response mechanisms in fruits with the ultimate aim of improving tolerance to low temperature and reduction of CI symptoms during cold storage.
Relationships between the race implicit association test and other measures of implicit and explicit social cognition
Authors: Pennington Charlotte R. , Ploszajski Matthew , Mistry Parmesh , NgOmbe Nicola , Back Charlotte , Parsons Sam , Shaw Daniel J.
Published: July 2023

Background

The race-based Implicit Association Test (IAT) was proposed to measure individual differences in implicit racial bias subsumed within social cognition. In recent years, researchers have debated the theoretical tenets underpinning the IAT, questioning whether performance on this task: (1) measures implicit attitudes that operate automatically outside of conscious awareness; (2) reflects individual differences in social cognition; and (3) can predict social behavior. One way to better address these research questions is to assess whether the race-IAT correlates with other implicit processes that are subsumed within social cognition.

Aims

The current study assessed whether the race-IAT was related to other commonly used individual difference measures of implicit (and explicit) social cognition. Experiment 1 assessed whether dissociable patterns of performance on the race-IAT were related to measures of implicit imitative tendencies, emotion recognition and perspective taking toward White task actors, as well as explicit measures of trait and state affective empathy and racial bias. Overcoming limitations of task conceptual correspondence, Experiment 2 assessed whether these latter tasks were sensitive in detecting racial biases by using both White and Black task actors and again examined their relationships with the race-IAT.

Method

In two lab-based experiments, 226 and 237 participants completed the race-IAT followed by an extensive battery of social cognition measures.

Results

Across both experiments, pro-White/anti-Black bias on the race-IAT was positively related to a pro-White bias on explicit measures of positive affective empathy. However, relationships between the race-IAT and implicit imitative tendencies, perspective taking, emotion recognition, and explicit trait and negative state affective empathy were statistically equivalent.

Conclusion

The race-IAT was consistently related to explicit measures of positive state affective empathy but not to other individual difference measures of implicit social cognition. These findings are discussed with regards to the theoretical underpinnings of the race-IAT as an individual difference measure of implicit social cognition, as well as alternative explanations relating to the reliability of social cognition measures and the various combinations of general-purpose (social and non-social) executive processes that underpin performance on these tasks.
Emotional cherry picking: the role of personality and goal orientation in selective emotion regulation for musical practice
Authors: Breaden Madden Gerard , Herff Steffen A. , Beveridge Scott , Jabusch Hans-Christian
Published: July 2023

Introduction

Emotion regulation is an important part of optimising performance and successful goal pursuit in practice-based tasks such as making music. Musicians may regulate their own emotions during the course of their musical practice in order to improve their performance and ultimately attain their practice-related goals. The specific emotions they target may depend upon their personality traits but may also relate to the nature of their goal orientation, and the interaction between the two. This study investigates whether the emotions desired by musicians in their musical practice were dependent on their personality traits and Mastery goal orientation (the desire to master musical and technical skills).

Methods

Via an online questionnaire, 421 musicians completed a personality scale and answered questions relating to their mastery practice goals. They also completed emotion scales indicating how strongly they desired to increase or decrease the intensity of specific emotions when practicing.

Results

Overall, musicians preferred to up-regulate positive rather than negative emotions [paired t(420) = 58.13, p < 0.001]. Bayesian Mixed Effects models showed that personality traits affected musicians’ desire to regulate specific emotions. For example, higher levels of Agreeableness predicted greater desire to increase positive but not negative emotions, whereas Extraversion predicted greater desire to increase anger [Est. = 0.05, SE = 0.03, Odds (Est. > 0) = 43.03] but not positive emotions. The inclusion of Mastery goal orientation either amplified or mitigated these effects in several cases, and also introduced new trait-emotion relationships. Findings confirm a general hedonic principle underlying the emotions musicians desired in their musical practice. However, predicted by personality traits, musicians also sometimes sought to increase the intensity of unpleasant emotions.

Discussion

These findings complement existing research that suggests that some Mastery-oriented musicians may seek an emotional state consisting of both positive and negative emotions. This and future studies on this topic may contribute to a better understanding of individual differences in emotion regulation ability as a potential aspect of individualised musical practice strategies.
Patterns of engagement in a digital mental health service during COVID-19: a cohort study for children and young people
Authors: Bernard Aynsley , de Ossorno Garcia Santiago , Salhi Louisa , John Ann , DelPozo-Banos Marcos
Published: July 2023

Introduction

The COVID-19 pandemic increased public use of digital mental health technologies. However, little is known about changes in user engagement with these platforms during the pandemic. This study aims to assess engagement changes with a digital mental healthcare service during COVID-19.

Methods

A cohort study based on routinely collected service usage data from a digital mental health support service in the United Kingdom. Returning users aged 14–25 years that interacted for a maximum of two months were included. The study population was divided into pre-COVID and COVID cohorts. Demographic and usage information between cohorts were compared and usage clusters were identified within each cohort. Differences were tested using Chi-squared, two-sample Kolmogorov–Smirnov tests and logit regressions.

Results

Of the 624,103 users who joined the service between May 1, 2019, and October 1, 2021, 18,889 (32.81%) met the inclusion criteria: 5,048 in the pre-COVID cohort and 13,841 in the COVID cohort. The COVID cohort wrote more journals; maintained the same focus on messaging practitioners, posting discussions, commenting on posts, and having booked chats; and engaged less in writing journals, setting personal goals, posting articles, and having ad-hoc chats. Four usage profiles were identified in both cohorts: one relatively disengaged, one focused on contacting practitioners through chats/messages, and two broadly interested in writing discussions and comments within the digital community. Despite their broad similarities, usage patterns also exhibited differences between cohorts. For example, all four clusters had over 70% of users attempting to have ad-hoc chats with practitioners in the pre-COVID cohort, compared to one out of four clusters in the COVID cohort. Overall, engagement change patterns during the COVID-19 pandemic were not equal across clusters. Sensitivity analysis revealed varying strength of these defined clusters.

Discussion

Our study identified changes in user activity and engagement behavior within a digital mental healthcare service during the COVID-19 pandemic. These findings suggest that usage patterns within digital mental health services may be susceptible to change in response to external events such as a pandemic. Continuous monitoring of engagement patterns is important for informed design and personalized interventions.
Time doesn’t heal all: PTSD symptoms exacerbate the relationship between age and pain intensity
Authors: O’Connor Victoria , Rowland Jared A. , Naylor Jennifer C. , Magnante Anna T. , Craig Katherine M. , Miskey Holly M. ,   VA Mid-Atlantic MIRECC Workgroup
Published: July 2023

Objective

Posttraumatic stress disorder (PTSD) symptoms and pain interfere with daily functioning and quality of life for many combat Veterans. As individuals age, pain symptoms tend to increase whereas PTSD symptoms tend to decrease. PTSD symptoms exacerbate pain, but the nature of this relationship across the aging process is unclear. The purpose of this study was to determine how PTSD symptoms affect the association between age and pain intensity.

Methods

Participants in this cross-sectional study included 450 Veterans (80% male) who served after September 11, 2001. PTSD and pain intensity ratings were assessed by the PTSD Checklist for DSM-5 (PCL-5) and the Brief Pain Inventory (BPI), respectively. Hierarchical multiple linear regression evaluated main and interaction effects between age, PTSD symptoms, and pain intensity.

Results

Age (B = 0.04, p < 0.001) and PTSD symptoms (B = 0.05, p < 0.001) were positively associated with pain intensity. Age and PTSD symptoms were inversely correlated (r = −0.16, p < 0.001). PTSD symptoms exacerbated the relationship between age and pain intensity (ΔR2 = 0.01, p = 0.036). Specifically, when greater PTSD symptoms were reported at older ages, pain intensity was significantly higher.

Conclusion

Results of these analyses suggests that age is important when considering the effects of PTSD symptoms on pain intensity ratings. Specifically, pain intensity ratings are higher in older Veterans with PTSD symptoms. These findings underscore the importance for clinical providers to evaluate trauma history and PTSD symptoms in older Veterans reporting pain symptoms.
Pragmatism or idealism: a systematic review and visual analysis of Winnicott’s psychoanalytical treatment views
Authors: Xie Zhengyan , Yan Yuting , Peng Kejuan
Published: July 2023

Winnicott is an outstanding representative of the School of Object Relations, and his unique psychoanalytic treatment views have been greatly influential to the psychoanalytical community. Winnicott emphasizes the impact of facilitating environment and the key role of the maternal-child relationship in the early psychological growth of individuals. He puts forward the ideas of the development mechanism of the true self and the false self, which builds a bridge between the characteristics of adult psychopathology and the characteristics of early maternal-child relationships, providing a new perspective for research on individual self-development and psychoanalysis. Winnicott creatively introduces the concepts of the transitional object and the transitional phenomena into the theories of Object Relations. He relates the transitional experience to the field of mental health, and extends it from the relationship between the mother and the child to adult life, which not only has had a revolutionary impact on modern psychoanalysis but also literature, aesthetics, and other fields. Winnicott highlights the importance of the patient’s emotional development in the treatment. He advocates holding the patients’ sentiments and meeting their emotional needs. He also approves of the emotional reparenting of the patients, to make them gain the ability to establish a relationship with the real world. His treatment views formed through a large number of clinical practices are very practical and full of humanistic care. This review summarizes Winnicott’s psychoanalytical treatment views as well as his marvelous original concepts, and analyzes the hot topics of academic research on his theories based on a visualization analysis by using the software CiteSpace, which includes data in the Web of Science Core Collection published from 1978 to 2023 with 365 papers involved. The study provides a macroscopic and panoramic review of Winnicott’s theories, and it clearly shows Winnicott’s significant influence on the field of psychoanalysis and related fields.
Association between work stress and mental health in Chinese public health workers during the COVID-19 epidemic: mediating role of social support and self-efficacy
Authors: Dong Yinqiao , Zhu Qianqian , Chang Ruijie , Wang Rongxi , Cai Yong , Huang Hong
Published: July 2023

Background

Little is known about the mediating mechanisms underlying the association between work stress and mental health, especially among primary public health workers (PHWs). We aimed to evaluated the association between work stress and mental health among PHWs, and explore the mediating roles of social support and self-efficacy.

Methods

A large-scale cross-sectional survey was conducted among 3,809 PHWs from all 249 community health centers in 16 administrative districts throughout Shanghai, China. Pearson correlation and hierarchical linear regression were used to explore the associations among work stress, social support, self-efficacy and mental health. Structural equation modeling (SEM) was conducted to examine the mediation effects.

Results

The prevalence of depression and anxiety symptoms among primary PHWs was 67.3 and 55.5%, respectively. There is a significant positive direct effect of work stress on mental health (β = 0.325, p < 0.001). Social support and self-efficacy partially mediated the relationship between work stress and mental health, respectively. Meanwhile, the chained mediating effects of social support and self-efficacy also buffered the predictive effects of work stress on anxiety and depression symptoms (β = 0.372, p < 0.001).

Conclusion

Work stress has significant direct and indirect effects on mental health among primary PHWs. Enhancing social support and self-efficacy may be effective psychological interventions to mitigate the effects of work-related stress on mental health. These findings highlight the severity of mental health problems among primary public health workers and provide new evidence for early prevention and effective intervention strategies.
Counterfactual learning in enhancing resilience in autonomous agent systems
Authors: Samarasinghe Dilini
Published: July 2023

Resilience in autonomous agent systems is about having the capacity to anticipate, respond to, adapt to, and recover from adverse and dynamic conditions in complex environments. It is associated with the intelligence possessed by the agents to preserve the functionality or to minimize the impact on functionality through a transformation, reconfiguration, or expansion performed across the system. Enhancing the resilience of systems could pave way toward higher autonomy allowing them to tackle intricate dynamic problems. The state-of-the-art systems have mostly focussed on improving the redundancy of the system, adopting decentralized control architectures, and utilizing distributed sensing capabilities. While machine learning approaches for efficient distribution and allocation of skills and tasks have enhanced the potential of these systems, they are still limited when presented with dynamic environments. To move beyond the current limitations, this paper advocates incorporating counterfactual learning models for agents to enable them with the ability to predict possible future conditions and adjust their behavior. Counterfactual learning is a topic that has recently been gaining attention as a model-agnostic and post-hoc technique to improve explainability in machine learning models. Using counterfactual causality can also help gain insights into unforeseen circumstances and make inferences about the probability of desired outcomes. We propose that this can be used in agent systems as a means to guide and prepare them to cope with unanticipated environmental conditions. This supplementary support for adaptation can enable the design of more intelligent and complex autonomous agent systems to address the multifaceted characteristics of real-world problem domains.
Diversified agriculture leads to diversified diets: panel data evidence from Bangladesh
Authors: Mastura Tamanna , Begum Ismat Ara , Kishore Avinash , Jackson Tamara , Woodhill Jim , Chatterjee Kuhu , Alam Mohammad Jahangir
Published: July 2023

This study used a panel data model to examine the relationship between agricultural diversification and dietary diversity of farm households across three waves of nationally representative Bangladesh Integrated Household Survey data (BIHS- 2011-12, 2015, and 2018). Prior research measured diversification in terms of crop cultivation and livestock rearing. However, this study takes a different approach to measuring agricultural diversification by combining the three major sectors—crop, fish, and livestock production—at the farm household level to evaluate the impact of such agricultural diversification on the diversification of diets in households. The panel data allows us to establish that agricultural production diversification has a significantly positive effect on the dietary diversity of farm households. Moreover, other important factors that impact agricultural diversification to improve dietary diversity, like women’s employment, market access, engagement with non-farm income sources, and access to information also have a strong association in improving the dietary status, food and nutritional security of households. Participation in the market helps farming households to become more commercially oriented but negatively affects the dietary diversification of the households. However, participation in non-farming activities was reported to have a significant positive influence on dietary diversity, though not as much as agricultural diversification. From the perspective of policy that requires nutrition into consideration, the findings suggest to focus on providing support for diversified farming systems can directly increase the nutritionally enriched dietary intake, increasing the employability of women. Promoting market participation through modern infrastructural facilities should be prioritized to improve the current scenarios.
Establishing the Swiss Spinal Tumor Registry (Swiss-STR): a prospective observation of surgical treatment patterns and long-term outcomes in patients with primary and metastatic spinal tumors
Authors: Nevzati Edin , Poletti Nicolas , Spiessberger Alexander , Bäbler Sabrina , Studer Gabriela , Riklin Christian , Diebold Joachim , Chatain Grégoire P. , Finn Michael , Witt Jens-Peter , Moser Manuel , Mariani Luigi
Published: July 2023

Background

Tumors of the vertebral column consist of primary spinal tumors and malignancies metastasizing to the spine. Although primary spine tumors are rare, metastases to the spine have gradually increased over past decades because of aging populations and improved survival for various cancer subtypes achieved by advances in cancer therapy. Metastases to the vertebral column occur in up to 70% of cancer patients, with 10% of patients demonstrating epidural spinal cord compression. Therefore, many cancer patients may face spinal surgical intervention during their chronic illness; such interventions range from simple cement augmentation over decompression of neural elements to extended instrumentation or spinal reconstruction. However, precise surgical treatment guidelines do not exist, likely due to the lack of robust, long-term clinical outcomes data and the overall heterogeneous nature of spinal tumors. Objectives of launching the Swiss Spinal Tumor Registry (Swiss-STR) are to collect and analyze high-quality, prospective, observational data on treatment patterns, clinical outcomes, and health-related quality of life (HRQoL) in adult patients undergoing spinal tumor surgery. This narrative review discusses our rationale and process of establishing this spinal cancer registry.

Methods

A REDCap-based registry was created for the standardized collection of clinical, radiographic, surgical, histological, radio-oncologial and oncological variables, as well as patient-reported outcome measures (PROMs).

Discussion

We propose that the Swiss-STR will inform on the effectiveness of current practices in spinal oncology and their impact on patient outcomes. Furthermore, the registry will enable better categorization of the various clinical presentations of spinal tumors, thereby facilitating treatment recommendations, defining the socio-economic burden on the healthcare system, and improving the quality of care. In cases of rare tumors, the multi-center data pooling will fill significant data gaps to yield better understanding of these entities. Finally, our two-step approach first implements a high-quality registry with efficient electronic data capture strategies across hospital sites in Switzerland, and second follows with potential to expand internationally, thus fostering future international scientific collaboration to further push the envelope in cancer research.
Integrated Conceptual Design and Parametric Control Assessment for a Hybrid Mobility Lunar Hopper
Authors: Rimani Jasmine , Bucchioni Giordana , Ryals Andrea Dan , Viola Nicole , Lizy-Destrez Stéphanie
Published: July 2023

The lunar lava tubes are envisioned as possible hosting structures for a human base in the Moon’s equatorial regions, providing shelter from radiations, micrometeoroids, and temperature excursion. A first robotic mission is set to scout the habitability of these underground architectures in the near future. The communication inside these underground tunnels is heavily constrained; hence, the scouting system should rely on a high degree of autonomy. At the same time, the exploration system may encounter different types of terrain, requiring an adaptable mobility subsystem able to travel fast on basaltic terrain while avoiding considerable obstacles. This paper presents a cave explorer’s mission study and preliminary sizing targeting the lunar lava tubes. The study proposes using a hybrid mobility system with wheels and thrusters to navigate smoothly inside the lava tubes. The peculiar mobility system of the cave explorer requires an accurate study of the adaptability of its control capabilities with the change of mass for a given set of sensors and actuators. The combination of conceptual design techniques and control assessment gives the engineer a clear indication of the feasible design box for the studied system during the initial formulation phases of a mission. This first part of the study focuses on framing the stakeholders’ needs and identifying the required capabilities of the cave explorer. Furthermore, the study focuses on assessing a design box in terms of mass and power consumption for the cave explorer. Following different mission-level assessments, a more detailed design of the cave explorer is discussed, providing an initial design in terms of mass and power consumption. Finally, the objective shifts toward studying the performances of the guidance, navigation, and control (GNC) algorithms varying the mass of the cave explorer. The GNC significantly impacts the design box of the surface planetary system. Hence, investigating its limitations can indicate the feasibility of mass growth to accommodate, for example, more payload.
Efficacy of Contact Insecticides for the Control of the Larger Grain Borer, Prostephanus truncatus (Horn), on Stored Maize
Authors: Panagiotakis Anastasios , Baliota Georgia V. , Rumbos Christos I. , Athanassiou Christos G.
Published: July 2023

One of the most destructive insect species for stored maize is the larger grain borer, Prostephanus truncatus. Its control is challenging, as it seems to have a natural tolerance to active ingredients that are effective for other stored-product insect species that cause infestations in maize. The objective of the present study was to comparatively evaluate a wide range of insecticides that are currently in use in stored product protection for the control of P. truncatus. Specifically, three inert dusts—namely, a kaolin, a zeolite and a diatomaceous earth formulation—and three residual insecticides—i.e., the pyrethroid deltamethrin, the bacterial insecticide spinosad and the juvenile hormone analogue S-Methoprene—were evaluated against adults of P. truncatus. Adult mortality was assessed after 7, 14, 21 and 28 d of exposure, whereas progeny production was measured after an additional interval of 28 d for inert dusts and 65 d for the contact insecticides. Moreover, the number and weight of infested and uninfested kernels per vial was measured. Low mortality levels were recorded for the three inert dusts even for the highest application rate and after 28 d of exposure. Moreover, the inert dusts tested failed to suppress the progeny production of P. truncatus. In contrast, high mortality levels were recorded for deltamethrin and spinosad that exceeded 95% already after 7 d of exposure at the lowest application rate (0.5 ppm). The application of S-Methoprene did not result in high adult mortality rates, irrespective of the application rate and the evaluation interval. Deltamethrin, spinosad and S-Methoprene significantly suppressed progeny production of the species at the doses tested.
Areas of Agrochemical Deepening Resulting from Long-Term Experiments with Fertilizers—Synthesis Following 20 Years of Annual and Stationary Fertilization
Authors: Rusu Mihai , Mihai Mihaela , Mihai Valentin C. , Moldovan Lavinia , Ceclan Ovidiu Adrian , Toader Constantin
Published: July 2023

The paper hereby focuses on the essential field of soil evolution in relation to the effect of long-term fertilization on plant yields and the essential, evolutionary, and impactful changes in their fertility. NP fertilization (by application of ammonium nitrate and concentrated superphosphate) causes a change in soil reaction over time through acidification with increasing N doses, while phosphorus is able to partially mitigate this process. Acidic soil—the typical preluvosol—as a result of adsorbed acidity activation, solubilizes Al ions and may become interested in calcic amendment. Acidification tendencies are also maintained in the amended variants, but to a lesser extent due to the neutralizing capacity of the amendment and the presence of calcium ions. Complex NP fertilization in acid soil (preluvosol) cultivated with wheat can maintain a relatively constant humus content (%), but calcium amendment can cause a reduction of this indicator. In contrast, under maize, due to the effects of conventional tillage and intensive tillage, fertilization contributes to a reduction in this indicator, which is more active against the background of limestone amendment. The phenomenon can be explained by potentiation through the mineralization of the organic component of the soil. This effect is diminished in alluvial mollisol with a higher humus content, saturated in bases, and a buffering capacity. Organic and organo-mineral fertilization can control the modeling of soil humus content and its agrochemical status. The regime of essential elements (N, P, and K) becomes active in mobile forms, and the precarious supply conditions of the initial stage tend to improve. Applied superphosphate evolves, differentiated from the applied dose and pH, into non-occluded mineral forms (P-Al, P-Fe, and P-Ca), which supply the mobile forms in the soil solution for the plants, with the importance of maintaining, more of these forms at the level required by plants. In the case of potassium, the dynamics of its forms in the soil (unchangeable and exchangeable) control the soil supply state of this element and the effect of its application to plants. The data presented show that long-term experiments can effectively support the study of soil fertility through the soil-plant relationship.
Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture with Transfer Learning Approach
Authors: Gogoi Munmi , Kumar Vikash , Begum Shahin , Sharma Neelesh , Kant Surya
Published: July 2023

Rice is a vital crop for global food security, but its production is vulnerable to various diseases. Early detection and treatment of rice diseases are crucial to minimise yield losses. Convolutional neural networks (CNNs) have shown great potential for disease detection in plant leaves, but training CNNs requires large datasets of labelled images, which can be expensive and time-consuming. Here, we have experimented a 3-Stage CNN architecture with a transfer learning approach that utilises a pre-trained CNN model fine-tuned on a small dataset of rice disease images. The proposed approach significantly reduces the required training data while achieving high accuracy. We also incorporated deep learning techniques such as progressive re-sizing and parametric rectified linear unit (PReLU) to enhance rice disease detection. Progressive re-sizing improves feature learning by gradually increasing image size during training, while PReLU reduces overfitting and enhances model performance. The proposed approach was evaluated on a dataset of 8883 and 1200 images of disease and healthy rice leaves, respectively, achieving an accuracy of 94% when subjected to the 10-fold cross-validation process, significantly higher than other methods. These simulation results for disease detection in rice prove the feasibility and efficiency and offer a cost-effective, accessible solution for the early detection of rice diseases, particularly useful in developing countries with limited resources that can significantly contribute toward sustainable food production.
The Spatio-Temporal Patterns and Influencing Factors of Different New Agricultural Business Entities in China—Based on POI Data from 2012 to 2021
Authors: Wei Wei , Yin Guanyi , Xie Shuai , Sun Qingzhi , Zhang Zhan , Li Guanghao
Published: July 2023

The high-quality development of new agricultural business entities (NABEs) is an important driving force for realizing rural revitalization and accelerating the modernization of agriculture and rural areas. The main purpose of the study is to investigate the spatial distribution pattern, aggregation scales, development mechanism, and internal differences of various types of NABEs in different regions. It provides targeted ideas for alleviating regional differences in the development of NABEs in different agricultural regions. Kernel density estimation, nearest neighbor distance analysis, Tyson’s polygon coefficient of variation, and Ripley’s K function are used to study the spatial and temporal evolution, spatial aggregation, and scale divergence of various types of NABEs, and Pearson correlation analysis is incorporated to explore the specific factors affecting the development of various types of NABEs. The study results: First, family farms are the most widely distributed, and agricultural enterprises are the most sparsely distributed, being distributed “more in the southeast and less in the northwest” in all three categories. Second, the strongest aggregation scales of different NABEs are increasing, and the strongest aggregation scales of agricultural enterprises are larger than those of family farms and cooperatives in all agricultural areas. Third, the development of specialized farmers’ cooperatives (abbreviated as ‘cooperatives’) is more constrained by traditional agricultural inputs and is a kind of agricultural input-oriented development. Family farms are more constrained by the living standards of rural residents in the region and are a kind of rural economy-oriented development. Agricultural enterprises are more subject to the economic level of the region, which is a kind of market economy-oriented development. Finally, in the process of developing NABEs, regional differences should be emphasized, and a small number of agriculturally leading enterprises and model cooperatives should drive a large number of small-scale family farms and smallholder farmers in order to become a characteristic path for China’s agricultural development.
Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates
Authors: Ghobadi Mahboobe , Gheysari Mahdi , Shayannejad Mohammad , Dokoohaki Hamze
Published: July 2023

Decision support system tools such as crop models and considering the uncertainties associated with them are important for making an informed decision to fill the yield gap in farms and increase food security. This study’s objective was to identify and quantify the degree to which crop management practices, as well as climate and soil, affected the uncertainty of total biomass, evapotranspiration, and water productivity of silage maize by using a crop model and spatiotemporal input data. Using a calibrated crop model (DSSAT) and pSIMS platform, three planting dates by considering ten ensemble weather data and three soil profile data were simulated for the time period between 2002 and 2017 with a 2 km × 2 km resolution across maize production areas with arid and Mediterranean climates in Isfahan province, Iran. Additionally, the findings were used to determine the yield gap in the studied area to identify opportunities to boost food production. Our results showed larger uncertainty in Mediterranean climates than in arid climates, and it was more affected by planting date than weather parameters and soil profile. The accuracy of total biomass prediction by using pSIMS-CERES-Maize based on the spatiotemporal input data was 1.9% compared to field experimental data in the dry climate, and the yield gap based on the comparison of modified-pSIMS-CERES-Maize and reported biomass was 6.8 to 13 tons ha−1 in the arid and Mediterranean climate. Generally, all results represented the importance of using crop models and considering spatiotemporal data to increase reliability and accuracy, especially in Mediterranean climates, and their potential to increase food production in developing countries with limited water resources and poor agriculture management.
Optimizing Nitrogen Regime Improves Dry Matter and Nitrogen Accumulation during Grain Filling to Increase Rice Yield
Authors: Zhou Shenqi , Liu Kun , Zhuo Xinxin , Wang Weilu , Zhang Weiyang , Zhang Hao , Gu Junfei , Yang Jianchang , Liu Lijun
Published: July 2023

Nitrogen (N) fertilizer is a critical element that affects rice yield. However, its effects on dry matter accumulation (DMA), N accumulation, and their physiological mechanisms with grain yield and N utilization efficiency still lack in-depth study. Three large-scale japonica rice varieties—Jinxiangyu 1, Nanjing 46, and Huaidao 5—were used in two field experiments with varying N fertilizer application rates to examine grain yield and N utilization efficiency. The results showed that: (1) In the range of 0~360 kg ha−1 total N application rate (TNAR), the rice yields of the three cultivars were maximum under the TNAR at 270 kg ha−1. The optimal TNAR for the highest yield of Jinxiangyu 1, Nanjing 46, and Huaidao 5 were calculated based on quadratic regressions with values of 305.5 kg ha−1, 307.6 kg ha−1, and 298.0 kg ha−1, and the corresponding yields were 10.3 t ha−1, 10.6 t ha−1 and 10.2 t ha−1, respectively. The N utilization efficiency decreased gradually with the increase in TNAR, and the recovery efficiency decreased from 35.7~38.19% to 29.61~31.59%. (2) The yield was significantly positively correlated with DMA and N accumulation from the heading stage (HD) to the maturity stage (MA). The DMA and N accumulation of HD-MA were significantly positively correlated with leaf photosynthetic rate, non-structural carbohydrate (NSC) accumulation in stems, root oxidation activity, zeatin (Z) + zeatin riboside (ZR) contents in roots, and nitrate reductase (NR) and glutamate synthase (GOGAT) activity in HD. (3) In the range of 0~216 kg ha−1 panicle N application rate (PNAR), the rice yield was maximum under the PNAR at 108 kg ha−1. The optimal PNAR for the highest yield of Jinxiangyu 1 was calculated based on the quadratic regression with values of 139.5 kg ha−1, and the highest yield was 9.72 t ha−1. The leaf photosynthetic rate, NSC accumulation in stems, root oxidation activity, Z + ZR contents in roots, and NR activity in leaves in rice were higher under 108 kg ha−1 PNAR. Excessive application of panicle fertilizer reduced the above physiological indicators and rice yield. The above results showed that optimizing N fertilizer could increase the leaf photosynthetic rate, NSC accumulation in stems, root oxidation activity, Z + ZR contents in roots, and NR activity from HD to MA, which was beneficial to improving DMA and N uptake during HD-MA, thus improving grain yield and N utilization efficiency in rice.
Pseudomonas fluorescens RB5 as a Biocontrol Strain for Controlling Wheat Sheath Blight Caused by Rhizoctonia cerealis
Authors: Yi Yanjie , Hou Zhipeng , Shi Yu , Zhang Changfu , Zhu Lijuan , Sun Xinge , Zhang Rumeng , Wang Zichao
Published: July 2023

Wheat sheath blight is a soil-borne fungal disease caused by Rhizoctonia cerealis and is a serious threat to wheat worldwide. A microbial fungicide is a promising alternative to a chemical fungicide for wheat disease control. In this study, strain RB5 against R. cerealis was isolated from wheat rhizosphere soil, which was identified as Pseudomonas fluorescens according to physiological, biochemical, and 16S rRNA gene sequence analyses. For improving the antifungal activity of RB5, the response surface methodology (RSM) was used to optimize the culture conditions for strain RB5, and the optimal culture conditions are 8.7 g/L of cassava, 5.2 g/L of soybean meal, pH 6.8, a 218 r/min speed, a 31.5 °C temperature, and 54 h of culture time. The inhibition rate of the culture filtrate obtained under this culture condition was up to 79.06%. The investigation of action mechanism showed strain RB5 could produce protease, chitinase, and siderophore, and its culture filtrate disrupted the mycelial morphology and inhibited the activities of three cell-wall-degrading enzymes of R. cerealis. Furthermore, the pot experiment exhibited that RB5 significantly controlled the wheat sheath blight with an efficacy of 71.22%. The evaluation of toxicological safety on an animal indicated that the culture filtrate was safe on mice. Overall, the culture filtrate of RB5 is a very promising microbial fungicide for the control of wheat sheath blight.
Response of Soil Absolute and Specific Enzyme Activities to Long-Term Application of Organic Fertilizer to Solonetz in Northeast China
Authors: Liu Danyang , Feng Andi , Li Senmiao , Song Bo , He Yujie , Lv Yunhao , Luo Jingmei , Liu Yang , Ma Xianfa , Li Xinyang
Published: July 2023

Soil organic carbon (SOC) and microbial biomass carbon (MBC) are highly correlated with enzyme activities. Specific enzyme activities can exclude the autocorrelation between enzyme activity and SOC and MBC. However, the responses of absolute and specific enzyme activities to saline–alkali properties remains unclear. In this study, the absolute and specific enzyme activities of cellobiose hydrolase, β-glucosidase, arylsulfatase, alkaline phosphatase, and urease were measured in soils with 10, 15, 18, 21, and 26 years of organic fertilizer application in contrast to soils without organic fertilizer application. The results showed that long-term organic fertilizer application led to significantly increased in the absolute and specific enzyme activity and decrease in pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), as well as sodium adsorption ratio (SAR5:1). In the structural equation model (SEM), the EC extremely limited the geometric mean of specific enzyme activity per unit of MBC (MBC-GMSEA) (path coefficient, −0.84, p < 0.001). Pearson’s correlation analysis showed that the correlations between EA/MBC and pH, EC, ESP, and SAR5:1 were higher than between the absolute soil enzyme activity and pH, EC, ESP, and SAR5:1. Of the parameters tested, EA/MBC was a more sensitive index to reflect the improvement effect of organic fertilizer on soils and evaluate the saline–alkali barrier.