The determinants of successful partnership models between coffee farmers and key stakeholders—comprising private enterprises, cooperatives, and governmental bodies—were investigated to enhance productivity and sustainability within the coffee sector in Mojokerto, Indonesia. A mixed-methods approach was employed, integrating factor analysis and multiple linear regression modeling to examine the predictive influence of partnership dimensions. Four core dimensions—economic, social, cultural, and agroclimatic—were evaluated through exploratory factor analysis to uncover latent structures underpinning partnership success. The analysis resulted in the identification of four principal components: socio-economic exchange dynamics, socio-economic connectivity of agriculture, capital networks and socio-economic experience, and economic and educational networks. These components were subsequently used as independent variables in a multiple linear regression model, where partnership success was operationalized through kernel weight outputs as a proxy for productivity performance. The regression model accounted for 84.19% of the variance in partnership success, indicating strong explanatory power. The findings underscore the critical role of non-economic dimensions—particularly social connectivity and education—in driving effective partnerships, alongside traditional economic considerations. Policy implications include the need to design intervention strategies that enhance farmers' access to capital, strengthen educational and training programs, and encourage participation in socio-economic networks. While the model demonstrates strong internal validity within the context of the coffee industry, its applicability to other agricultural commodities remains to be tested. Further research is recommended to validate these findings across diverse agro-industrial contexts, thereby supporting the development of inclusive and scalable partnership models. This study contributes empirical evidence to inform stakeholder decision-making and promote resilient, equity-driven frameworks for agricultural collaboration.

Shallot farming in Indonesia has significant risks, primarily due to production variability and price instability. These risks deter farmers from adopting strategies involving higher risk tolerance levels. Risk aversion varies across individuals, leading to differences in decision-making processes. The study examines income risk levels in shallot farming. It explores farmers' behaviors in response to these risks in two distinct regions: the highlands of Karanganyar Regency, Central Java, and the lowlands of Bantul Regency, Daerah Istimewa Yogyakarta. A total of 200 shallot farmers were randomly selected for structured interviews to assess their risk behavior and the factors influencing it. The analysis reveals that shallot farming entails a high degree of income risk, and the highland areas exhibit a greater coefficient of variation (0.574) compared to the lowlands (0.544). Approximately 65% of highland farmers and 80% of lowland farmers were observed to be risk-averse concerning their shallot farming activities. Key factors influencing risk behavior include land size, household size, farming experience, age, frequency of crop failure, education, income, and farming location. Notably, farming experience, education, household size, and income positively impact risk behavior, increasing farmers' likelihood of adopting risk-taking strategies. The primary source of income risk was production variability, exacerbated by staggered planting schedules. This study highlights the importance of synchronizing planting schedules and strengthening farmer group networks to improve planning, marketing, input procurement, and knowledge exchange. The findings also provide a foundation for policymakers to design regulations that optimize planting times and mitigate income risks in shallot farming.

Seed Quality is an important area of agriculture and directly influences crop yield and germination percentage. Visual examination forms the foundation of traditional seed testing techniques, which are cumbersome, inflexible, and inefficient for effective assessment. This study proposed an automated approach to seed quality assessment based on physical measurement using machine learning and image processing techniques. Snapshots of the new seeds were captured and underwent feature extraction, segmentation, and image improvement to explore notable morphological attributes, such as size and colour. To tag seeds as "good" or "bad" based on physical characteristics, Support Vector Machines (SVMs) are used as a reference model. Rather, Convolutional Neural Networks (CNNs) have been utilised for deep feature extraction and classification. Experimental findings indicate that CNNs perform better than conventional machine learning models, with a scalable and highly accurate method of seed quality assessment. Future use will utilise quantum machine learning to improve prediction and facilitate sustainable, precision agriculture. The improved framework, optimised with great care for onion seeds, is a major breakthrough in increasing the agricultural productivity of onion cultivation.

The rice crisis represents a significant threat to food security and economic stability in Southeast Asia, a region where rice serves as the primary staple for the majority of the population. This crisis is exacerbated by a confluence of factors, including climate change, crop failures, and restrictive export policies, as exemplified by the El Niño phenomenon and India’s 2023 rice export ban. Rising rice prices have been linked to increased social unrest, with the potential to trigger widespread demonstrations across affected nations. To proactively address this issue, the restlessness indicator was introduced as a predictive tool, integrating key variables such as rice prices, consumption patterns, and per capita income. This study employs a Spatio-Temporal Autoregressive (STAR) model to forecast restlessness values across six Southeast Asian countries—Indonesia, the Philippines, Thailand, Vietnam, Malaysia, and Cambodia—from 2024 to 2028. The STAR (5,1) model was identified as the optimal framework, achieving a Mean Absolute Percentage Error (MAPE) of 15.1%. The forecasting results indicate that none of the analyzed countries are projected to enter a state of unprecedented restlessness during the specified period, suggesting that no severe rice crisis is anticipated within this timeframe. These findings provide critical insights for policymakers and stakeholders, enabling the development of preemptive strategies to mitigate potential food security challenges. The study underscores the utility of the restlessness indicator as a robust tool for monitoring and forecasting rice-related crises, contributing to the broader discourse on sustainable food systems in Southeast Asia.

The incorporation of Azolla into soil was investigated in this study for its potential to enhance soil fertility by influencing key parameters, including organic carbon (Organic-C) content, total nitrogen (Total-N), and cation exchange capacity (CEC). This study was conducted in a controlled greenhouse environment using a Completely Randomized Design (CRD) with eight treatments and three replications. The primary objective was to evaluate the effects of Azolla on soil quality, particularly in improving organic matter content and nitrogen (N) retention, both of which are essential for sustainable agricultural management. The findings indicate that Azolla incorporation led to a 29% increase in soil Organic-C and a 21% increase in Total-N compared to control treatments (p < 0.05). Additionally, CEC was enhanced by 33.4%, demonstrating improved nutrient retention capacity. A strong positive correlation was observed between Organic-C content, soil pH, and CEC, suggesting that Azolla contributes to optimizing soil nutrient dynamics. These results highlight the capacity of Azolla to function as a biofertilizer, improving soil fertility and nitrogen cycling while reducing dependence on synthetic fertilizers. The potential of Azolla to serve as an eco-friendly amendment aligns with sustainable agricultural practices aimed at enhancing soil health and long-term productivity. The findings contribute to the growing body of research on biofertilizers, offering valuable insights for soil management strategies that prioritize environmental sustainability and resource efficiency.

Organic rice farming in Kulonprogo Regency, recognized by the Indonesian government as a pioneering region for organic agriculture, has been increasingly adopted as a sustainable agricultural practice. However, the implementation of organic rice farming systems continues to face significant challenges that hinder their full potential. This study evaluates the sustainability of organic rice farming in Kulonprogo Regency using the Multidimensional Scaling (MDS) technique, facilitated by the Rapfish application, across three critical dimensions: ecological, economic, and social. Data were collected from 70 respondents across three farmer groups—Srijati, Tegal Mulyo, and Jatingarang Lor—selected through a census method, all of which have fully transitioned to organic rice farming practices. The sustainability indices derived from the analysis revealed that the ecological dimension scored 87.79%, indicating a "sustainable" status, while the economic and social dimensions scored 52.35% and 71.61%, respectively, both categorized as "quite sustainable." These findings underscore the ecological robustness of organic rice farming in the region while highlighting the need for targeted interventions to enhance economic viability and social acceptance. Overall, the sustainability level of organic rice farming in Kulonprogo Regency was classified as "moderately sustainable." The validity of the analysis was confirmed through a Standardized Residual Sum of Squares (STRESS) value of 0.25%, which is categorized as "excellent," ensuring the reliability and accuracy of the results. This study provides critical insights into the sustainability dynamics of organic rice farming, offering a foundation for policymakers and stakeholders to develop strategies that address existing challenges and promote long-term sustainability in the region. These findings indicate the urgency of implementing sustainable agricultural practices in organic rice cultivation, as well as the strategic role of government support in price stabilization policies, waste management, and equitable program distribution to strengthen the economic, ecological, and social aspects of the sustainable agricultural system.

The Khyndogny grape variety, indigenous to the Karabakh region of Azerbaijan, represents a vital component of the area’s viticultural heritage. Despite its historical and cultural significance, the cultivation and development of Khyndogny face numerous challenges. This study aims to comprehensively evaluate the morphological, biological, and technological characteristics of Khyndogny, with a particular focus on its enocarpological and enochemical properties, population structure, biotypic diversity, and clonal variations. Field and laboratory analyses were conducted, integrating morphological assessments, physicochemical evaluations, and comparative studies with the locally cultivated Madrasa and the globally recognized Cabernet Sauvignon varieties. Advanced analytical techniques were employed to quantify critical parameters such as sugar content, titratable acidity, and other factors influencing wine quality and yield potential. The findings reveal that Khyndogny outperforms both Madrasa and Cabernet Sauvignon across multiple quality metrics, achieving a superior overall evaluation score of 7.22, compared to 6.70 and 5.78, respectively. The variety’s high yield potential and suitability for premium winemaking were demonstrated, underscoring its value for sustainable viticulture and regional economic development. However, challenges related to genetic variability, environmental adaptation, and cultivation practices were identified, necessitating further research and innovation. This study contributes to a deeper understanding of grape biodiversity in Karabakh and provides actionable insights for the promotion of Khyndogny in both local and global wine markets. Limitations related to sample size and environmental variability were acknowledged, and future research directions were proposed, including long-term adaptation strategies, genetic improvement programs, and market expansion initiatives. By addressing these challenges, Khyndogny has the potential to emerge as a cornerstone of high-quality viticulture and a driver of economic growth in the region.

Farmer regeneration has been recognized as a critical factor in ensuring sustainable agricultural development and preserving ecological and cultural heritage. This study aims to: (i) examine the socio-demographic characteristics of rice farmers, (ii) assess the state of farmer regeneration, and (iii) analyze the labor requirements within the rice farming sector in West Denpasar District, Denpasar City, Bali, Indonesia. A mixed-method approach was employed, combining structured and in-depth interviews with 187 farmers, selected using the Slovin formula with a 5% margin of error and a 95% confidence level from a population of 352 farmers. Quantitative and qualitative data were collected through interviews and surveys to evaluate the agricultural system and workforce dynamics. The findings indicated that 59.36% of respondents identified farming as their primary occupation. However, a significant majority of their children pursued non-agricultural professions, citing the preference for stable income and professional careers. While most farmers endeavored to instill ecological values and emphasized the cultural significance of rice farming, 74.86% reported that their children had no engagement in agricultural activities. The labor force in rice farming primarily comprised family members supplemented by hired workers, particularly during labor-intensive periods. The employment of external workers was necessitated by extensive landholdings and the operational demands of mechanized and manual farming practices. These findings underscore the importance of addressing generational shifts in farming participation to ensure the sustainability of agricultural productivity and cultural heritage. Farmer regeneration was identified as pivotal to enhancing agricultural output, fostering ecological conservation, and improving community food security, while simultaneously addressing broader socio-economic challenges.

The migratory locust has become a principal threat to agricultural production in Kazakhstan, exacerbating risks posed by drought, fires, and other natural disasters. The widespread and persistent use of chemical pesticides, often referred to as "chemical press" tactics, has been the conventional approach to locust control. However, such methods are associated with escalating costs, environmental contamination, and the development of pesticide resistance. In response, alternative strategies that focus on preventive control and localized treatment have been explored. This approach aims to mitigate the negative ecological impacts of indiscriminate pesticide application by targeting locust populations with biological control agents and low-toxicity plant protection products in identified foci. This study evaluates the use of unmanned aerial vehicles (UAVs) for the precision application of biopesticides in Kazakhstan's locust-affected regions. Orthophotographic maps generated from UAV imagery were employed to detect locust presence based on indirect diagnostic indicators. These maps facilitated the identification of locust aggregations, which were subsequently treated with biologically based pest management solutions. The effectiveness of UAV-based localized treatments was assessed, demonstrating significant control over locust populations in focal areas, with minimal environmental impact. The results suggest that UAV technology, combined with targeted biological treatments, offers a promising alternative to traditional chemical methods, potentially reducing pesticide resistance and ecological harm while maintaining effective pest control. This approach aligns with sustainable agricultural practices and enhances the efficiency of pest management strategies in locust-prone regions.

Soil salinization is a critical global issue that undermines agricultural productivity, threatens food security, and compromises the sustainability of farming systems. In the coastal region of Navotas, Balayan, Batangas, Philippines, the increasing salinization of irrigated rice farmlands due to saline-sodic soils has become a pressing concern. The understanding of farmers' perceptions regarding the risks associated with soil salinity, as well as their insights into the causes, consequences, and coping mechanisms, remains inadequately explored. This gap in knowledge may hinder the development of effective countermeasures and appropriate policy interventions. In this study, farmers' perceptions of saline-sodic soils were assessed through Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs), which were analysed using thematic analysis and Causal Loop Diagrams (CLDs). Saltwater intrusion was consistently identified as the primary cause of soil salinization, with subsequent reductions in rice yields ranging from 60% to 100%, depending on the growth stage of the crop. The most vulnerable periods were identified as June to August, particularly during the vegetative and flowering stages of rice. Farmers reported coping strategies such as irrigation with freshwater and the cultivation of salt-tolerant rice varieties; however, challenges related to delayed access to freshwater and seeds were frequently mentioned. The expansion of saline-sodic areas was identified as the most significant threat to rice production, with the condition of saline-sodic soils in lowland rice paddies increasingly viewed as a worsening problem. These findings underscore the severe impact of saline-sodic soils on both agricultural productivity and farmers' livelihoods, providing valuable insights that can inform the development of tailored management strategies and mitigation measures.

The sustainability of organic rice farming has become a significant focus in agricultural development, as it addresses the interconnected challenges of economic viability, environmental preservation, and social equity. This study evaluates the sustainability of organic rice farming across five districts in Central Java and Yogyakarta, Indonesia, through a comprehensive assessment of economic, ecological, and social dimensions. A proportional stratified random sampling approach was employed, involving 150 farmer respondents, with 30 farmers selected from each district. Descriptive analysis revealed an average sustainability score of 2.94, indicating a moderate level of sustainability. In addition, the Rapid Appraisal for Sustainability (RAPS) tool yielded an average index score of 68.56, categorising the farming systems as "fairly sustainable." The model was further validated through a normalization test, which demonstrated strong consistency across the three sustainability dimensions, with a Standardized Residual Sum of Square (STRESS) value of 0.14 and an R-Squared (RSQ) value of 0.95, suggesting that the data were robust and the model reliable. Sensitivity analysis identified seven critical factors influencing sustainability: agricultural product prices, financial management, poverty alleviation, crop rotation, the involvement of women and young farmers, and preservation of tradition. The results of validation and stability tests indicated that the sustainability model was both stable and reliable across all three dimensions, with an overall sustainability score of 1. These findings underscore the importance of promoting sustainable agricultural practices in organic rice farming and highlight the need for enhanced government involvement in raising awareness, providing training, and fostering educational initiatives to support the economic, ecological, and social dimensions of sustainability in the region.