Hasil untuk "Labor systems"

U. Mogili, B. Deepak

Abstract In the present era, there are too many developments in precision agriculture for increasing the crop productivity. Especially, in the developing countries like India, over 70% of the rural people depends upon the agriculture fields. The agriculture fields faces dramatic losses due to the diseases. These diseases came from the pests and insets, which reduces the productivity of the crops. Pesticides and fertilizers are used to kill the insects and pests in order to enhance the crop quality. The WHO (World Health Organization) estimated as one million cases of ill effected, when spraying the pesticides in the crop filed manually. The Unmanned aerial vehicle (UAV) – aircrafts are used to spray the pesticides to avoid the health problems of humans when they spray manually. UAVs can be used easily, where the equipment and labors difficulty to operate. This paper reviews briefly the implementation of UAVs for crop monitoring and pesticide spraying.

Erik Brynjolfsson, L. Hitt

Nada O. Eissa, Jeffrey B. Liebman

G. Robinson

T. Piketty, Emmanuel Saez, Stefanie Stantcheva

N. Gomez‐Lopez, Derek StLouis, Marcus A Lehr et al.

Labor resembles an inflammatory response that includes secretion of cytokines/chemokines by resident and infiltrating immune cells into reproductive tissues and the maternal/fetal interface. Untimely activation of these inflammatory pathways leads to preterm labor, which can result in preterm birth. Preterm birth is a major determinant of neonatal mortality and morbidity; therefore, the elucidation of the process of labor at a cellular and molecular level is essential for understanding the pathophysiology of preterm labor. Here, we summarize the role of innate and adaptive immune cells in the physiological or pathological activation of labor. We review published literature regarding the role of innate and adaptive immune cells in the cervix, myometrium, fetal membranes, decidua and the fetus in late pregnancy and labor at term and preterm. Accumulating evidence suggests that innate immune cells (neutrophils, macrophages and mast cells) mediate the process of labor by releasing pro-inflammatory factors such as cytokines, chemokines and matrix metalloproteinases. Adaptive immune cells (T-cell subsets and B cells) participate in the maintenance of fetomaternal tolerance during pregnancy, and an alteration in their function or abundance may lead to labor at term or preterm. Also, immune cells that bridge the innate and adaptive immune systems (natural killer T (NKT) cells and dendritic cells (DCs)) seem to participate in the pathophysiology of preterm labor. In conclusion, a balance between innate and adaptive immune cells is required in order to sustain pregnancy; an alteration of this balance will lead to labor at term or preterm.

Andrea Della Valle, Sara De Carlo, Gregorio Sonsini et al.

Abstract The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressive-like behaviours in rodents. Over the last 10 years, more than 5500 scientific articles reporting the use of the FST have been published. Despite its widespread use, the FST behaviours are still manually scored, resulting in a labor-intensive and time-consuming process that is prone to human bias and variability. Despite eliminating some biases, existing automated systems are costly and typically only able to distinguish between immobility and active behaviours. Therefore, they are often unable to accurately differentiate the major subtypes of movement patterns, such as swimming and climbing. To address these limitations, we propose a novel approach based on machine learning (ML) using a three-dimensional residual convolutional neural network (3D RCNN) that processes video pixels directly, capturing the spatiotemporal dynamics of rodent behaviour. Our ML model was validated against manual scoring in rats treated with fluoxetine and desipramine, two antidepressants known to induce distinct behavioural patterns. The ML model successfully differentiated among swimming, climbing, and immobility behaviours, demonstrating its potential as a standardized and unbiased tool for automatized behavioural analysis in the FST. Subsequently, we successfully validated our model by testing its ability to distinguish between drugs that predominantly evoke climbing (i.e., amitriptyline), those that preferentially facilitate swimming (i.e., paroxetine), and those that evoke both in a more balanced manner (i.e., venlafaxine). This approach represents a significant advancement in preclinical research, providing a more accurate and efficient method to analyze forced swimming data in rodents. We anticipate that in addition to the FST, our model and approach could be extended for application to various behavioural tests in laboratory animals, by training with specific datasets.

Ruby Putt, Hubert Brouwers, Peter John Groves et al.

Cage-free egg production is now the predominant system in Australia. However, the occurrence of floor eggs (FE), which are eggs laid outside designated nest boxes, presents a major challenge for these producers. To understand factors that may be associated with the laying of FE, a national scoping survey of cage-free egg-laying flocks was undertaken. Forty-three flocks across multiple farms were surveyed via a phone-based interview using predetermined questions. Floor egg levels ranged from 0.01–17%. There was no difference in floor egg levels between the breeds of brown-egg-laying hens. Age at peak lay did not alter the level of FE, but higher rate of peak lay had a weak association with fewer FE (r = −0.31, <i>p</i> = 0.049). Larger flocks had a lower percentage of FE (r = −0.5, <i>p</i> = 0.002), and farmers of larger sized flocks considered a lower level of floor eggs to be acceptable. Farms with tunnel-ventilated sheds reported fewer FE compared to those using other ventilation systems (<i>p</i> = 0.013). Higher floor egg levels were associated with increased labor costs (<i>p</i> = 0.023). These findings suggest that shed design and environmental management may be leveraged to reduce floor egg occurrence and improve operational efficiency in cage-free systems.

Gunhee Kim, Junsik Shin, Jongbin Won et al.

Bearings play a crucial role in mitigating loads, maintaining stability, and transferring forces between superstructures and substructures. However, bearing failures caused by external factors can compromise structural safety. Therefore, continuous monitoring of bearing displacement is essential, yet current inspection methods are labor-intensive and unsuitable for long-term management. To address this, researchers have proposed systems such as Linear Variable Differential Transformers (LVDTs) and computer vision-based monitoring methods to track bearing displacement over time. However, reliance on external power sources and complex installation processes has limited their widespread application. This paper proposes an automated monitoring system integrating low-power IoT sensors, computer vision, and cloud computing. The system features an event-driven power mechanism to minimize energy consumption and utilizes vision-based displacement measurement techniques, providing both portability and efficiency. Applied in a real-world setting for nine months, the system successfully enabled the long-term monitoring of bridge bearings. The results demonstrate its effectiveness in overcoming traditional limitations and highlight its potential in supporting automated, data-driven assessments of structural stability.

Rahmawati Nur, Musta'anah Himmayatul

This study aims to analyze the economic feasibility of organic and conventional rice farming in Sleman Regency, Indonesia. The analysis compared production costs, revenues, income, profits, and overall economic feasibility between the two farming systems. The research was conducted purposively in Widodomartani and Sumberharjo Villages. A census method was employed to include all 30 organic rice farmers, while 33 conventional farmers were selected using a proportional random sampling method. Data were analyzed using a quantitative descriptive approach on a 1,000 m² land basis. The results showed that organic rice farming incurred higher production costs than conventional farming but also generated greater revenue, income, and profit. The analysis revealed that both systems were economically feasible, as reflected by R/C ratios greater than one, with values of 1.55 for organic and 1.50 for conventional rice farming. In terms of capital, land, and labor productivity, both systems outperformed local economic references, such as interest rates, land rent, and minimum wage, with organic farming achieving relatively higher values across all indicators. Therefore, encouraging the broader adoption of organic farming through policy support, farmer training, and sustainable agricultural initiatives is essential to enhance profitability while maintaining the environment and promoting long-term agricultural sustainability.

Yongqing Ben, Yongqing Ben, Yu'e Zhang et al.

IntroductionReconciling food security with economic development amid rapid industrialization and urbanization presents a critical global challenge. This study investigates the spatiotemporal dynamics of grain production and its spatial interaction with economic development in Jiangsu Province, China—an economically advanced region exemplifying this tension.MethodsWe integrate the Gini coefficient, concentration index, standard deviational ellipse, spatial exploratory analysis (global/local Moran's I), and a Spatial Durbin Model (SDM) to quantify spatial differentiation patterns and spillover effects.Results(1) Pronounced spatial polarization emerged: Northern Jiangsu consolidated as a High-High grain production cluster, while Southern Jiangsu evolved into a Low-Low cluster. The spatial divergence between economic and grain production centroids expanded to 125.4 km. (2) Spatial econometrics confirmed localized suppression of grain output by economic development, alongside positive spillovers to neighboring regions—validating core-periphery complementarity. Urbanization drove sown area contraction via labor migration and cropland conversion. (3) Cultivated land endowment and rural labor were fundamental pillars of food security. Industrial restructuring indirectly enhanced production through land efficiency gains.DiscussionThe findings validate core-periphery theory and reveal complex spatial spillovers. Policy prescriptions include: spatial governance mechanisms coordinating regional specialization; industrial feedback systems reinvesting economic gains into agriculture; a Technology-Driven Resource Breakthrough strategy; and institutional safeguards for cropland. This establishes a replicable paradigm for food security-economic growth synergies in developing economies.

Diep Dao Mong, Thuong Mai Thi

This study aimed to explore the relationship between three factors: enforcement of labor social welfare labor laws, internal corporate social responsibility (CSR) implementation, and job satisfaction as perceived by managers in the commercial banking sector. The research utilized a qualitative research method – in-depth interviews based on a semi-structured questionnaire with 20 experienced managers from 11 commercial banks in Vietnam, an emerging economy. Findings indicated a positive reciprocal relationship between enforcement of social welfare labor laws and internal CSR responsibilities of enterprises. Additionally, enforcing labor social welfare laws and internal CSR implementation positively impacted employee job satisfaction. The study also identified five aspects of internal CSR implementation towards employees: (1) timely and full payment of wages, bonuses, and benefits; (2) establishing a conducive work environment; (3) policies addressing human rights, health, and safety at work; (4) fair and democratic treatment, providing training and career advancement opportunities, and protecting employees through organizational activities; (5) having legal norms, procedures, mechanisms for recording, feedback, monitoring, and reasonable evaluation of job performance. These findings contribute to enriching both theoretical understanding and practical implications regarding the interplay of these three factors in commercial banks, encouraging managers to effectively implement social welfare laws and internal CSR implementation. AcknowledgmentThis collaborative research involves scholars from the University of Law - Hue University and Duy Tan University. The authors extend their gratitude to both institutions for their support and assistance in facilitating the publication of this research. In addition, the authors would like to thank the Editor-in Chief and a reviewer for their helpful comments that in our view have helped to improve the quality of the manuscript significantly. This study was conducted based on decision No. 4741/QD-ĐHDT dated October 18, 2023 of Duy Tan University, Vietnam.

M. Carmen Cobo, William J. Farris, Chandler J. Olson et al.

Solenogastres is a group of mollusks with evolutionary and ecological importance. Nevertheless, their diversity is underestimated and knowledge about the distribution of the approximately 300 formally described species is limited. Factors that contribute to this include their small size and frequent misidentification by non-specialists. Recent deep-sea explorations have resulted in the collection of numerous specimens through effective methods such as epibenthic sledges. However, this is a costly, labor-intensive, and destructive methodology. In contrast, Autonomous Reef Monitoring Structures (ARMS) offer a novel, non-destructive approach, by providing a substrate for benthic organism colonization. This study is the first to describe Solenogastres collected using ARMS, demonstrating that they are an effective tool for biodiversity assessment and characterizing rare marine invertebrates. Following an integrative taxonomic approach, two new solenogaster species are described: Dondersia tweedtae Farris, Olson &amp;amp; Kocot, sp. nov. (Dondersiidae) and Eleutheromenia bullescens Cobo, sp. nov. (Pruvotinidae). The diagnosis of the family Dondersiidae is amended and the necessity of reassessing the validity of the current diagnostic characters for Pruvotinidae, and its classification is emphasized. The two newly described species exhibit distinct external characteristics; D. tweedtae sp. nov. has a striking pink color with a bright yellow dorsal keel and E. bullescens sp. nov. has a unique, discontinuous dorsal keel with nearly spherical protrusions. The presence of cnidocytes in the digestive systems of both species indicate that they feed on cnidarians. It is hypothesized that, like in some nudibranchs, their coloration and body features reflect defensive adaptations related to their diet. This study shows that while habitus alone is typically insufficient for accurate identification in solenogasters, it can sometimes simplify the process. For this, live observations and photographs are essential.

Jiefei Liu, Derek W. Bailey, Huiping Cao et al.

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.

Samsuzzaman, Md Nasim Reza, Sumaiya Islam et al.

Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R² up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity.

Eric Yang, Laura Katz, Sushila Shenoy

<h4>Objective</h4>The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches.<h4>Methods</h4>Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field.<h4>Results</h4>With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods.<h4>Conclusion</h4>Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials.

Rupak Goswami, Kalyan Roy, S. Dutta et al.

The shock of Coronavirus Disease 2019 (COVID-19) has disrupted food systems worldwide. Such disruption, affecting multiple systems interfaces in smallholder agriculture, is unprecedented and needs to be understood from multi-stakeholder perspectives. The multiple loops of causality in the pathways of impact renders the system outcomes unpredictable. Understanding the nature of such unpredictable pathways is critical to identify present and future systems intervention strategies. Our study aims to explore the multiple pathways of present and future impact created by the pandemic and “Amphan” cyclonic storm on smallholder agricultural systems. Also, we anticipate the behaviour of the systems elements under different realistic scenarios of intervention. We explored the severity and multi-faceted impacts of the pandemic on vulnerable smallholder agricultural production systems through in-depth interactions with key players at the micro-level. It provided contextual information, and revealed critical insights to understand the cascading effect of the pandemic and the cyclone on farm households. We employed thematic analysis of in-depth interviews with multiple stakeholders in Sundarbans areas in eastern India, to identify the present and future systems outcomes caused by the pandemic, and later compounded by “Amphan”. The immediate adaptation strategies of the farmers were engaging family labors, exchanging labors with neighbouring farmers, borrowing money from relatives, accessing free food rations, replacing dead livestock, early harvesting, and reclamation of waterbodies. The thematic analysis identified several systems elements, such as harvesting, marketing, labor accessibility, among others, through which the impacts of the pandemic were expressed. Drawing on these outputs, we employed Mental Modeler, a Fuzzy-Logic Cognitive Mapping tool, to develop multi-stakeholder mental models for the smallholder agricultural systems of the region. Analysis of the mental models indicated the centrality of “Kharif” (monsoon) rice production, current farm income, and investment for the next crop cycle to determine the pathways and degree of the dual impact on farm households. Current household expenditure, livestock, and soil fertility were other central elements in the shared mental model. Scenario analysis with multiple stakeholders suggested enhanced market access and current household income, sustained investment in farming, rapid improvement in affected soil, irrigation water and livestock as the most effective strategies to enhance the resilience of farm families during and after the pandemic. This study may help in formulating short and long-term intervention strategies in the post-pandemic communities, and the methodological approach can be used elsewhere to understand perturbed socioecological systems to formulate anticipatory intervention strategies based on collective wisdom of stakeholders.

M. Khairul Anam, Indra Prayogo, Susandri et al.

Currently, Smart Schools have been widely applied in several schools, within the scope of education and services as they are being encouraged to support Smart City. Smart Schools is a school concept utilizing information technology used in the teaching and learning process in the class and school administration. One of the schools in Pekanbaru City that will implement smart schools is Junior High School 17 Pekanbaru. The aspect of building smart schools themselves is not only adequate infrastructure such as servers, labor, and integrated systems but also the readiness on the part of schools and students in implementing Smart Schools in the future. Therefore, to find out the level of readiness of prospective users of the Smart Schools concept, the technology readiness index (TRI) method with four personality variables; optimism, innovativeness, discomfort, and insecurity was used. The purpose of this research was to find out the readiness index of prospective users in the implementation of Smart Schools and see what factors need to be improved from the readiness of prospective users. The results show that teachers and students are ready to apply new technologies in an effort to implement smart schools at Junior High School 17 Pekanbaru. This can be seen from the results obtained, namely the optimism and innovation variables received medium to high ratings. for the discomfort and insecurity to be completely low. However, the student guardians are still unsure because all variables get medium scores. From these results it was stated that Junior High School 17 Pekanbaru was ready to apply new technology for implementing smart schools. In addition, this research can also serve as a guideline for other junior high schools in analyzing new technology users, so that the applied technology can run well.

Ade Dwi sakti, Misra Hartati, Muhammad Nur et al.

The primary focus of this research revolves around the measurement of productivity and the factors that impact Crude Palm Oil (CPO) production which currently faces challenges in assessing whether existing productivity falls into the 'satisfactory' category. The contribution of this study is conducting a comprehensive productivity assessment, focusing on metrics and identifying the causes of productivity decline, especially potential points of failure. The measurement indicators use the Objective Matrix (OMAX) method which includes five ratios, including raw material utilization, energy consumption, labor efficiency, optimization of production targets, and production capacity utilization. The Analytical Hierarchy Process (AHP) is used to assign relative weight to factors that contribute to overall productivity. In addition, Failure Modes and Effects Analysis (FMEA) functions as a tool to determine the causes of decreased productivity by considering the potential for failure to occur. The research results show that productivity reached its highest point in March at 219.93% and the lowest in July at -67.33%. Based on the score assessment, this decline was mainly caused by the lack of optimal achievement of production targets, symbolized by a ratio of 4 to a score of 45. Potential causes for this ratio include non-compliance in selecting FFB that meets standards, and production standards that are too ambitious. targets, and mental and physical fatigue and stress. To improve overall performance, proposed improvements include the application of Internet of Things (IoT) technology, such as the use of sensors and automation systems in production processes, as well as investment in agricultural technology as a monitoring system. This increase is aimed at achieving higher production targets and overall efficiency.

Hari Sharma Neupane, Bikram Acharya, Pradeep Wagle et al.

Agriculture has been a cornerstone of human civilization for thousands of years, providing food and other essential resources to sustain our societies. However, as we enter the 21st century, we face unprecedented challenges that threaten the very foundations of our agricultural systems. Climate change, resource depletion, and population growth are just a few of the issues that demand urgent attention from policymakers and practitioners alike. Further, the growing population, climate change, the recent COVID-19 pandemic, the Ukraine-Russia war, and the depreciation of national currencies have disrupted the global food supply chain and increased food prices and food insecurity in many countries, including Nepal. The Nepalese agriculture sector alone contributed employment opportunities for more than 60 % of the population with a 23.9% share in total value added of the national economy (Ministry of Finance, 2022). Though the majority of farmers in Nepal are engaged in the agriculture sector, there is still a dominance of traditional and subsistence agriculture and the country's agricultural production is not enough to feed its population. The continued rise in import bills and volume of food products in recent years has been a major challenge for the country. Addressing these constraints warrants consortia of efforts from the government, nonprofits, and private sectors to promote sustainable and regenerative agricultural concepts and practices that align with local farm attributes and the agroecological environment.  With the above mentioned issue, Policy Research Institute, the publisher of NPPR, collaborated with Association of Nepalese Agricultural Professionals of Americas (NAPA) for the utilization of expert knowledge for public policy making and policy discussion. PRI is open to collaborating with any professional and intellectual society for policy issues. Thereof, a two-day (January 6-7, 2023) virtual symposium on "Agricultural Policies and Practices in Nepal: Pathways for Transformation" was jointly organized by the PRI and NAPA with the aim to discuss and synthesize structural, policy intervention-related procedural, and local barriers and issues inherent to inadequate agricultural growth in Nepal and recommend transformative and pragmatic policies, programs, and practices feasible at local, regional, and national levels. The other symposium collaborators were the Ministry of Agriculture and Livestock Development (MoALD), Nepal Agricultural Research Council (NARC), Agriculture and Forestry University (AFU), Institute of Agriculture and Animal Sciences (IAAS, Tribhuvan University), Nepal Agricultural Cooperative Central Federation Ltd. (NACCFL), and Society of Agricultural Scientists-Nepal (SAS-Nepal). The 38 papers presented at the symposium brought together over 500 researchers, policymakers, and practitioners from around the world. The symposium highlighted the importance of innovative policies and practices that can help transform agriculture and ensure its sustainability for future generations. The symposium was organized and facilitated in four thematic areas. The Agriculture Policy theme highlighted an analysis of current agricultural policies, laws, and regulations that have hindered the production and marketing of farm products, land use policies, transformative agriculture for the viable and circular economy, promoting cooperative farming, farm diversity, and sustainability including internationally successful policy practices suitable for Nepal. The Agricultural Research, Education, and Extension theme included diverse subject matters. These were genetic improvement of crops and livestock for diverse agro-climatic zones; technology innovations and dissemination; science-based knowledge and extension practices; climate-smart and organic agriculture; agri-business and entrepreneurship; commercial agriculture; and integration of agricultural research, education, and extension. Similarly, the Technology and Infrastructure Development theme focused on varied avenues of innovative technology (such as UAV, GIS, and Remote Sensing), farm mechanization, and smart and efficient irrigation practices to optimize costs of production, labor, fertilizer shortages, and monitoring of plant and soil health Finally, the Governance theme underpinned coherence and discordance between the policy frameworks and governing structures/mechanisms of three levels of government and opportunities for realignment for agricultural transformation as well as a local governance framework for agricultural service delivery at a municipality level. Finally, the symposium highlighted the importance of partnerships and collaborations in driving transformational change. The papers discussed the potential of public-private partnerships, multi-stakeholder platforms, and other forms of collaboration to leverage resources, share knowledge, and scale up innovative solutions. This special issue received 20 papers for publication consideration, however, after the review process, it  is able to manage 12 papers for publication. These papers provide a rich and diverse set of insights into the pathways for transforming agriculture. They offer both practical guidance and theoretical frameworks for policymakers and practitioners seeking to navigate the complex challenges facing agriculture today. We hope this special issue will inspire further research and action towards a more sustainable and equitable agricultural future. We thank all the authors who contributed to this special issue and the reviewers who provided their valuable feedback. We also extend our appreciation to the symposium organizers and collaborators. Finally, we encourage additional authors/presenters to submit their papers in the NPPR’s Regular Issue, which will be published in September 2023.