A decent income is an important part of overcoming economic disparities in agricultural development, especially in developing countries where most of the population are small farmers. As a developing country, Indonesia has also established a decent standard of living by setting a minimum wage as a reference for a decent income at the national and regional levels. However, this benchmark is not relevant to be applied uniformly at all levels of workers. This research determines the national coffee development area as the study center. We developed the Anker living wage methodology as a simple concept for determining living income for certain worker communities, especially for small farmers in rural areas who dominate the type of work in Indonesia. a socio-spatial approach is used to visualize the distribution of the dynamics of a decent life in various conditions of farming households. We found that 96.6% of coffee farming households in the national coffee development area had an inadequate living income, and only 3.4% were at an adequate level. We conclude that the current state of agricultural land management does not guarantee a decent income, even though efforts have been made to maximize agricultural crop productivity. The spatial description also shows that this condition is evenly distributed throughout residential areas. It is hoped that this approach can become an essential reference in implementing agricultural development programs that focus on welfare and equitable development as benchmarks for sustainable development goals in the future.

The purpose of this study is to explore factors influencing the blockchain adoption in agricultural supply chains, to make a particular focus on how security and privacy considerations, policy support, and management support impact the blockchain adoption intention. it further investigates perceived usefulness as a mediating variable that potentially amplifies the effects of these factors on blockchain adoption intention, and sets perceived cost as a moderating variable to test its influence on the strength and direction of the relationship between perceived usefulness and adoption intention. through embedding the cost-benefit theory into the integrated tam-toe framework and utilizing the partial least squares structural equation modeling (PLS-SEM) method, this study identifies the pivotal factors that drive or impede blockchain adoption in the agricultural supply chains, which fills the gap of the relatively insufficient research on the blockchain adoption in agriculture field. the results further provide empirical evidence and strategic insights that can guide practical implementations, to equip stakeholders or practitioners with the necessary knowledge to navigate the complexities of integrating cutting-edge technologies into traditional agricultural operations, thereby promoting more efficient, transparent, and resilient agricultural supply chains.

Mecula Haroano Laa is a local wisdom that includes beliefs, norms, and practices passed down from generation to generation in the context of agricultural resource preservation and community cultural identity formation. The author is interested in investigating the practices of the Mecula Haroano Laa tradition, which is unique to North Buton Regency and has unique specifications and characteristics. This research uses a qualitative approach. The data collection techniques used in this study are in-depth interviews and participatory observations. The results of this study demonstrate that Mecula Haroano Laa in North Buton society is more than just an agricultural custom; it is also an attempt to strengthen social solidarity among community members. This practice reflects the spirit of solidarity, gotong royong together, and respect for the environment. The North Buton community is actively involved in implementing Mecula Haroano Laa as a form of participation in developing sustainable agriculture. This research contributes to understanding the importance of local wisdom in building social cohesion in communities. Research implications include sustainable planning and efforts to empower communities in developing farms in North Buton Regency. Natural resource management policies may incorporate. Mecula Haroano Laa’s effective and sustainable resource management techniques to promote wise use, environmental conservation, economic resilience, and dependency reduction.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The role of agriculture in greenhouse gas emissions and carbon neutrality is a complex and important area of study. It involves both carbon sequestration, like photosynthesis, and carbon emission, such as land cultivation and livestock breeding. In Shandong Province, a major agricultural region in China, understanding these dynamics is not only crucial for local and national carbon neutrality goals, but also for global efforts. In this study, we utilized panel data spanning over two decades from 2000 to 2022 and closely examined agricultural carbon dynamics in 16 cities of the Shandong Province. The method from the Intergovernmental Panel on Climate Change (IPCC) was used for calculating agricultural carbon sinks, carbon emissions, and carbon surplus. The results showed that (1) carbon sink from crops in the Shandong Province experienced growth during the study period, closely associated with the rise in crop yields; (2) a significant portion of agricultural carbon emissions was attributable to gastrointestinal fermentation in cattle, and a reduction in the number of stocked cattle led to a fall in overall carbon emissions; (3) carbon surplus underwent a significant transition in 2008, turning from negative to positive, and the lowest value of carbon surplus was noticed in 2003, with agriculture sector reaching the carbon peak; (4) the spatial pattern of carbon surplus intensity distinctly changed before and after 2005, and from 2000 to 2005, demonstrating spatial aggregation. This research elucidates that agriculture in Shandong Province achieved carbon neutrality as early as 2008. This is a pivotal progression, as it indicates a balance between carbon emissions and absorption, highlighting the sector’s ability in maintaining a healthy carbon equilibrium.

Coordination and integration among farms within agri-food chains are crucial to tackle the issue of fragmentation within the primary sector, both at the European and national level. The Italian agri-food system still complains about the need to aggregate supply to support market dynamics, especially for niche and quality products that characterize the Made in Italy. It is well known that the Italian agri-food sector is closely linked to the relationship between agriculture on one hand and culture/tradition on the other, which is reflected in the high number of quality products that have obtained EU PDO (Protected Designation of Origin) and PGI (Protected Geographical Indication) recognition. The development of vertical forms of coordination has found significant support in recent years from the integrated supply chain design approach, which is increasingly becoming an essential tool for implementing rural development policies. In this context, the study provides a comparison between companies that have joined the Integrated Supply Chain Projects of the Rural Development Program and those that have not applied. The aim is to highlight any differences in order to understand policy impact. The analysis is based on the Emilia-Romagna region Farm Accountancy Data Network (FADN) data, and the sample consists of more than 2 thousand farms. The statistical analysis conducted compares treated and non-treated using the Welch-t-test for independent unmatched samples. The main results show higher values for treated farms when structural variables are analyzed, like the utilized agricultural area or the agricultural work unit. In general, higher balance sheet performances emerged for treated farms. In conclusion, this study shows that the Integrated Supply Chain Projects represent a worthwhile tool both to increase cooperation, food quality, and to enhance a competitive agricultural sector.