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Chrysanthemum is an ornamental plant widely cultivated in Indonesia, with various varieties that have unique and attractive characteristics. Each variety responds differently to changes in light intensity. Providing shade can help regulate the light intensity received by chrysanthemum seedlings during the acclimatization stage. This study aimed to determine the best shade percentage for the acclimatization growth of three chrysanthemum varieties. The research was conducted in the screenhouse of the Faculty of Agriculture, Tanjungpura University, for three months, from October to December 2024. The experiment used a Split Plot Design (Split Plot) with a Randomized Block Design (RBD), consisting of two factors: shade percentage and variety. There were three levels of shade and three chrysanthemum varieties, resulting in nine treatment combinations. Each treatment was repeated three times, with each replication consisting of three sample plants, resulting in 81 experimental units. The shade percentages used were 25% (n1), 50% (n2), and 75% (n3), and the varieties tested were Xanne (v1), Suciyono (v2), and Pinka Pinky (v3). Observed variables included plant survival rate, plant height, stem diameter, internode length, number of internodes, number of flower primordia, leaf color changes, along with supporting data such as temperature, humidity, and light intensity. The results showed that 50% shade was effective in promoting plant height and the number of flower primordia in the three varieties: Xanne, Suciyono, and Pinka Pinky. The Suciyono variety exhibited good vegetative and generative growth under all shade percentages.

The agricultural sector plays a strategic role in meeting food needs while maintaining environmental sustainability and community welfare. However, conventional farming practices that rely on chemical inputs have caused various problems, such as soil degradation, loss of biodiversity, and environmental pollution. This condition has encouraged the adoption of the Sustainable Agriculture concept, which emphasizes a balance between ecological, economic, and social aspects. Its successful implementation is strongly influenced by farmers’ perceptions as the main actors. This study aims to analyze rice farmers’ perceptions of the implementation of Sustainable Agriculture in swampy areas of Tebas District, Sambas Regency. The research was conducted from October to November 2025 using a survey method involving 95 farmers selected through the Slovin formula and proportional random sampling technique. Data were collected through observation, interviews, and Likert-scale questionnaires, and analyzed using descriptive analysis and binary logistic regression. The results show that farmers’ perceptions are generally positive, particularly regarding water management, variety selection, and cropping patterns. However, the use of organic fertilizers, environmentally friendly pest control, and post-harvest management are still considered difficult. Education level and non-farming occupations significantly influence farmers’ perceptions. Therefore, enhancing extension services, training, and support for business diversification is necessary to promote the adoption of Sustainable Agriculture.

This study aims to analyze the level of financial literacy and examine its influence on the household welfare of vegetable farmers. Agricultural households, particularly smallholder vegetable farmers, frequently experience economic vulnerability due to several structural challenges such as unstable agricultural income, seasonal production patterns, fluctuating market prices, limited access to formal financial services, and inadequate financial management skills. These conditions often make farm households more susceptible to economic shocks, including crop failure, input price increases, or sudden market price declines. In this context, financial literacy becomes an essential capability that enables farmers to manage their financial resources more effectively.This research employed a quantitative research design using a survey approach. The study involved 120 vegetable farmers selected as respondents from major vegetable-producing areas. Data were collected through structured questionnaires designed to measure farmers’ financial literacy levels and household welfare conditions. Financial literacy was assessed through indicators such as financial knowledge, financial behavior, and financial attitudes, while household welfare was evaluated based on indicators including consumption stability, education and health expenditures, savings capacity, and overall economic resilience. The collected data were analyzed using descriptive statistics to describe the characteristics and financial literacy levels of respondents, and multiple linear regression analysis to examine the relationship between financial literacy and household welfare.The results of this study highlight the importance of strengthening financial education programs targeted at agricultural communities. Improving financial literacy among vegetable farmers can contribute not only to better household financial management but also to broader rural economic development. Therefore, financial education initiatives should be integrated into agricultural extension programs, farmer group activities, and local government development strategies. Such initiatives may include training in household financial planning, simple bookkeeping for farm businesses, savings management, and responsible credit use. By enhancing farmers’ financial capabilities, these programs can help improve household welfare, strengthen rural economic resilience, and support the long-term sustainability of the agricultural sector.

Landslides in the Giritengah Catchment Area are influenced by several factors, including geological conditions, rainfall intensity, geomorphology, soil characteristics, and inappropriate land use practices, all of which affect regional spatial planning and environmental stability. This study presents a literature review that analyzes landslide vulnerability, evaluates the impact of land use changes, and proposes integrated Soil and Water Conservation Techniques (SWCT) to support sustainable land management. The analysis applies Geographic Information System methods using thematic map overlays such as rainfall distribution, slope gradient, geological structure, and land use patterns. The results show that areas categorized as having high landslide vulnerability cover 44.02% or approximately 158.69 hectares of the catchment area, while areas with very low vulnerability account for only 0.12% or about 0.79 hectares. Land use conversion, particularly mixed dryland agriculture, has increased landslide risk by reducing slope stability and increasing surface runoff. To address this issue, conservation strategies are recommended, including vegetative measures such as greening 38.51 hectares in settlement areas and implementing agroforestry systems across 218.48 hectares. In addition, structural measures such as three dam retainers and twenty gully plugs are proposed in both protected and cultivation zones to support disaster mitigation and align with regional spatial planning policies.

Conflicts in transmigration areas are generally multidimensional and influenced by social, economic, land, and institutional factors. This study aims to identify the forms and distribution of conflicts in three districts of the transmigration area, namely Momi Waren District, Ransiki District, and Oransbari District, as well as to formulate a smart system-based conflict resolution approach through the use of spatial data, local institutions, and local wisdom-based settlement practices. Based on field mapping, four main categories of conflict were identified: 1) Land conflicts occur throughout the transmigration sites in the form of claims to transmigration land that has not been handed over to transmigrants because the compensation price is below normal. In addition, there is no ATR BPN office in South Manokwari Regency, one of whose functions is community empowerment and conflict resolution. 2) Economic conflicts occur because transmigrants are registered and recorded in the population registry, making it easy for them to access capital. Several economic activities in agriculture and transportation services are dominated by transmigrants, causing economic jealousy. 3) Social conflicts occur when the distribution of social assistance is uneven and the excessive use of illegally sold alcoholic beverages causes social unrest. 4) Institutional conflicts occur when civil servants, police, and military personnel are recruited, and not all indigenous Papuans who are nominated can be accommodated, requiring the involvement of tribal councils to formulate recommendations for recruitment that prioritize indigenous Papuans. The root causes of the conflict were analyzed using a root cause analysis approach that covered unclear land boundaries, unequal economic access, weak coordination between institutions, and low social trust due to differences in interests between groups. This study utilizes best practices from the Tribal Council, the South Manokwari Regency Transmigration and Manpower Office, the Religious Harmony Forum, and the Social Services Office as the basis for developing smart maps for an early warning system for conflicts. The results of the study formulate a Smart Conflict Resolution System framework consisting of three main components: (1) participatory spatial mapping of conflicts and key actors, (2) integration of institutional databases and social-customary mediation channels, and (3) design of smart maps as a mitigation and decision-making tool in transmigration areas. This system is expected to strengthen collaborative governance, prevent conflict escalation, and realize inclusive and sustainable management of transmigration areas

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

Sweet corn is a horticultural commodity that is widely consumed in Indonesia. This study evaluated the effectiveness of NPK 18-18-18 fertilizer on the vegetative growth, yield components, and relative agronomic effectiveness (RAE) of sweet corn (Zea mays saccharata). Field experiments were conducted using fertilizer dosages of 0,50, 0,75, 1,00, and 1,50 NPK, with a control treatment for comparison. The application of NPK 18-18-18 significantly increased plant height, stem diameter, leaf number, ear length, ear diameter, biomass weight, ear weight with husk, ear weight without husk, plot yield, and overall productivity compared with the control. Mean values across treatments ranged from 68,94–205,72 cm for plant height, 7,41–20,47 mm for stem diameter, 6,01–13,00 leaves per plant, 15,41–20,89 cm for ear length, and 36,05–49,65 mm for ear diameter. Biomass weight ranged from 0,12–0,34 kg, ear weight with husk from 0,13–0,34 kg, and ear weight without husk from 0,12–0,28 kg. Plot yield varied between 7,91–25,46 kg, corresponding to productivity levels of 5,02–16,16 t/ha. RAE analysis indicated that fertilizer application was effective at dosages of 0,75, 1,00, and 1,50 NPK, with the highest effectiveness observed at 1,50 NPK (118%). Notably, the 0,75 NPK dosage achieved an RAE value of 101%, demonstrating that lower fertilizer input can enhance yield while reducing production costs and mitigating fertilizer scarcity. These findings suggest that NPK 18-18-18 fertilizer, when applied at an optimal dosage, can be effectively utilized in sweet corn cultivation to improve growth and productivity while ensuring efficient nutrient management.

This study aims to assess the growth rate of harvested area and rice production in Bengkulu Province during the 2019–2024 period and to examine spatial variation across districts as a basis for regional agricultural policy planning. The data used are secondary, consisting of a series of spatial and temporal data on harvested area and rice production; the analysis method includes calculating the compound annual growth rate (CAGR) for each indicator and examining temporal and spatial patterns across districts. The analysis results indicate a negative growth rate in the rice sector, with a relatively larger contraction in harvested area compared to production, accompanied by fluctuations and differences between years, indicating heterogeneity in agricultural performance at the district level. These findings have clear policy implications: the need for agricultural land protection measures, improvement and maintenance of irrigation infrastructure, increased farmer access to technology and markets, and the implementation of indicator-based growth rate monitoring to determine intervention priorities. To strengthen the policy base, recommended further research includes field verification and combined (quantitative-qualitative) studies to identify local factors driving the observed trends and fluctuations.

Cocoa farming is one of the plantation subsectors that plays a strategic role in Indonesia’s economy, as it contributes to increasing farmers’ income, national exports, and the development of the chocolate processing industry. Indonesia is recognized as one of the world’s largest cocoa producers, with major production areas located in Sulawesi, particularly South Sulawesi, Central Sulawesi, and Southeast Sulawesi. However, the sustainability of cocoa farming still faces various challenges, such as low crop productivity, the use of low-quality seedlings, suboptimal cultivation techniques, and the presence of pests and plant diseases. In addition, limited access to capital and the low level of farmers’ financial management skills also affect the sustainability of cocoa farming. Many farmers do not yet have proper farm financial record-keeping systems, making it difficult to manage production costs, cash flow, and farm capital planning. In this context, financial literacy becomes an important factor that can help farmers manage their farming activities more effectively and sustainably. This study aims to analyze the effect of financial literacy on the sustainability of cocoa farming and farmers’ welfare. The research employs a quantitative approach using a survey method involving 120 cocoa farmers in Sidenreng Rappang Regency (Sidrap), South Sulawesi. Data were collected through questionnaires and interviews and then analyzed using multiple linear regression analysis. The results indicate that financial literacy has a positive and significant effect on farm financial management and the sustainability of agricultural businesses. Farmers with higher levels of financial literacy tend to manage farm capital more effectively, maintain proper financial records, and improve farm productivity. Therefore, improving financial literacy can become

Accurate paddy field area data is a crucial factor in agricultural development planning, particularly as a basis for formulating data-driven programs and policies. One of the main problems encountered at the regional level is the continued reliance on administrative data that does not fully reflect actual field conditions due to land use changes, boundary inaccuracies, and limited verification processes. This study aims to determine an appropriate method for obtaining accurate paddy field area data through the application of Geographic Information Systems (GIS) in Maros Regency. The research employed a descriptive quantitative approach using spatial analysis methods. The data utilized consisted of Paddy Field Base Data, National Geospatial Base Maps, high-resolution satellite imagery, and field verification data. Spatial analysis was conducted through digitization, overlay, intersect, and area calculation processes using ArcMap 10.8 software. The results indicate that GIS-based spatial analysis produces more accurate and verified paddy field area data compared to conventional administrative data. The integration of spatial data with field verification effectively minimizes measurement errors and boundary discrepancies. This method can serve as a reliable reference for providing accurate agricultural base data to support planning and policy-making in the agricultural sector.

The issue of global food security is increasingly pressing amidst climate change, population growth, and environmental degradation. The agricultural sector, particularly rice production, faces threats from pests and diseases that reduce crop yields and farmer incomes. Climate change exacerbates pest attack patterns, increasing crop losses. In addition, excessive use of chemical pesticides leads to pest resistance and negative impacts on ecosystems and human health. This study used a descriptive method with a qualitative approach, and the study population included farmers who cultivate rice fields and farmer groups that have received Integrated Pest Management (IPM) in Makale District, Tana Toraja Regency, with a population of 325 families. Respondents were randomly selected at 15% of the total population, with a sample of 49 farmers consisting of three farmer groups. The results showed that the role of farmer groups in IPM implementation in Makale District includes extension and training (65.5%), facilitating access to information and resources (69%), decision-making (67.5%), and conflict management and IPM cooperation (66.5%). Therefore, it is necessary to strengthen the implementation of the rice farming system, with support from the Government and the Tana Toraja Regency Agriculture Service to optimize the development of rice farming businesses.

The purpose of this training is to provide understanding, competence and skills for training participants about the utilization of waste from broiler farms (litter) to be used as organic fertilizer, so that it can increase the income of farmers and can reduce negative impacts on the environment. The training method includes lectures, discussion, visits to broiler farm locations and practical work on making organic fertilizer for 30 training participants. Lectures and practical work on making organic fertilizer were carried out at the Animal Production Laboratory of the Animal Husbandry Department, Faculty of Agriculture, Pattimura University. The results of the activity showed an increase in understanding of the management of waste from broiler farms to be managed properly. The conclusion of the Community Service Program Training on Making Organic Fertilizer Using Broiler Manure is as follows: This training succeeded in increasing 92,56 percent the knowledge, competence and skills of participants regarding the processing of broiler waste (litter) into quality organic fertilizer; This training provides an effective solution to overcome the problem of broiler waste (litter) accumulation in the livestock environment; This training changed the perspective of training participants on producing and commercializing organic fertilizer, so that it can increase the income of farmers.

Agricultural practices that take local wisdom into account can provide significant benefits to the environment and society. Local wisdom plays a crucial role in sustainable agricultural practices. Time-tested knowledge and practices can provide solutions to the challenges faced by modern farmers, particularly in the context of climate change and environmental degradation. However, despite extensive research, a significant research gap remains, specifically how local wisdom in the Tumbang Datu Valley can be integrated into broader agricultural policies. The research used was descriptive with a qualitative approach. Qualitative research aims to understand social phenomena from the perspective of participants. Initial informants were selected purposively, selecting informants deemed to have extensive knowledge of the conditions in the village under study, using a snowball sampling technique. The first step was to identify key informants, who in this study consisted of 16 traditional leaders and farmers familiar with the culture in Lembang Tumbang Datu and directly involved in the practice. The local wisdom in the rice farming process, from pre-planting, planting, maintenance, to harvesting and post-harvest, is implemented based on ancestral heritage in line with environmental conservation. Farmers demonstrate their concern for nature through natural soil cultivation, selection of superior seeds, use of organic fertilizers, and implementation of efficient planting and irrigation systems. The application of Integrated Pest Management (IPM) and post-harvest technology demonstrates the farmers' ability to adapt to innovation while maintaining local wisdom. Overall, the rice farming system in Lembang Tumbang Datu demonstrates the synergy between tradition and modernity, supporting food security while preserving cultural heritage.

Dependence on chemical fertilizers has caused a decline in soil quality, groundwater contamination, and rising agricultural production costs due to unstable fertilizer prices. In recent years, frequent fertilizer crises have revealed the fragility of reliance on inorganic fertilizers within the national agricultural system. Hence, a transformation toward environmentally friendly and sustainable agriculture is urgently required. This study employed a scoring technique to analyze field observation data, which were narrated based on the written methodology. Respondents’ answers were categorized and classified according to their assumptions or opinions, with scores determined using a Likert scale. The Likert scale measures attitudes, opinions, and perceptions of individuals or groups regarding specific social phenomena. The results showed that key driving factors—such as affordable fertilizer prices, support from agricultural extension workers, and social encouragement from the community—achieved scores above 82%. This finding indicates that external conditions are quite favorable for promoting the use of organic fertilizers. However, major challenges remain, including the limited availability of organic fertilizers in the field and farmers’ long-standing dependency on chemical fertilizers. To address these challenges, it is essential to enhance the market availability of liquid organic fertilizers (POC) through collaboration between producers and farmer groups. Continuous technical assistance should be provided, including demonstration plots that display tangible improvements in rice yields using POC. Furthermore, government support in the form of targeted subsidies or special incentives for farmers transitioning to organic fertilizers is vital, while strengthening the role of farmer groups and agricultural extension workers as catalysts for the adoption of environmentally friendly innovations.

Tomato crop yields face significant threats from plant diseases, with existing deep learning solutions often computationally prohibitive for resource-constrained agricultural settings; to address this gap, we propose Efficient Disease Attention Network (EDANet), a novel lightweight architecture combining depthwise separable convolutions with hybrid attention mechanisms for efficient Tomato disease recognition. Our approach integrates channel and spatial attention within hierarchical blocks to prioritize symptomatic regions while utilizing depthwise decomposition to reduce parameters to only 104,043 (multiple times smaller than MobileNet and EfficientNet). Evaluated on ten tomato disease classes from PlantVillage, EDANet achieves 97.32% accuracy and exceptional (~1.00) micro-AUC, with perfect recognition of Mosaic virus (100% F1-score) and robust performance on challenging cases like Early blight (93.2% F1) and Target Spot (93.6% F1). The architecture processes 128×128 RGB images in ~23ms on standard CPUs, enabling real-time field diagnostics without GPU dependencies. This work bridges laboratory AI and practical farm deployment by optimizing the accuracy-efficiency tradeoff, providing farmers with an accessible tool for early disease intervention in resource-limited environments.

Kuala Indah Village in Sei Suka, Batubara, North Sumatra, is a coastal village whose economy relies on fisheries and aquaculture. The community is predominantly Muslim and also practices small-scale agriculture and animal husbandry. The Community Service Program (KKN) provides a platform for students to apply academic theory in real-world contexts, while also encouraging active community involvement. KKN students implement various programs, such as stunting prevention, religious moderation, skills training for poverty alleviation, and education for children and adolescents. They also play a role in increasing community participation through cultural activities and supporting local businesses by strengthening digital visibility. These initiatives support the creation of inclusive villages, providing equal access and opportunities regardless of gender, religion, or socioeconomic status. This collaborative approach reinforces the value of mutual cooperation and enriches social and educational life in the village. Through KKN, universities demonstrate their tangible contribution to village development. The success of this program underscores the importance of social inclusion and collective work in promoting sustainable development in rural areas like Kuala Indah.  

The aim of this research is to determine the effect of using NPK 16:16:16 fertilizer dosage on the growth and yield of white mustard (Brassica Pekinensis) ITTO variety to determine the effect of using organic kasgot biochar fertilizer on the growth and yield of white mustard (Brassica Pekinensis) ITTO variety  of white mustard (Brassica Pekinensis) ITTO variety to determine the interaction between the treatment of 16:16:16 fertilizer dosage and organic kasgot biochar fertilizer on the growth and yield of white mustard (Brassica Pekinensis) ITTO variety.This study was conducted in the land of UPT BIH (Horticulture Parent Seed Task Force Unit) Kutagadung, Berastagi District, Karo Regency, North Sumatra. The study was conducted in November 2023 - December 2023. This study used a factorial randomized block design consisting of two factors, namely: Factor I. Provision of Kasgot Biochar Fertilizer (K) with 4 levels, namely: K0 = Control, K1 = 1 kg / plot, K2 = 2 Kg / plot. Factor II. Application of NPK Fertilizer 16:16:16 (P) with 4 levels, namely N0 = Control N1 = 10 grams/plot, N2 = 20 grams/plot, N3 = 30 grams/plot The results of the study showed that the Use of Kasgot Biochar Fertilizer (K) was significantly different from plant height, plant diameter, leaf width, fresh weight per crop of sample plants, fresh weight of plants per plot and production per hectare. The best treatment was the K2 treatment (2 kg/plot). The use of NPK Fertilizer (N) was significantly different from plant height, plant diameter, leaf width, fresh weight per crop of sample plants, fresh weight of plants per plot and production per hectare. The best treatment of N-P-K 16-16-16 fertilizer was the N3 treatment (30 grams/plot), and the Interaction of the use of Kasgot Biochar Fertilizer (K) and NPK Fertilizer (N) was not significantly different from plant height, plant diameter, leaf width, fresh weight per crop of sample plants, fresh weight of plants per plot and production per hectare.