Information on land reserves is crucial and required to support agricultural development in relation to increased population pressure, food demand, and food security. This research aims to identify and evaluate idle abandoned land based on biophysical suitability, status of land concessions, and forest areas to determine potential land reserves for agricultural development to support food security in Indonesia. The results show that, at the national level, the area of suitable abandoned lands for agricultural extensification is 27.7 million ha, but most of these lands have concession permits and are located in forest areas, so 12.4 million ha are still available, with the largest area being in dry land. The identification of abandoned land by employing satellite imagery in 54 districts resulted in a smaller acreage compared to abandoned land being mapped formally at 1:250,000. After considering land ownership and forest status, both sources resulted in similar areas at a scale of 1:50,000 and 1:250,000, i.e., 6.1 million ha and 6.0 million ha, respectively. It seems that land ownership and forest status adjusted the total acreage of identified land reserves at different map scales. An area of around 7.4 million ha will be required to meet food demand in 2045 by assuming constant consumption per capita. We found about 1.7 million ha as potential land reserves, most of which are in conversion or production forests areas. Converting forests or utilizing drylands could be potential alternatives to deal with the lack of land for food production. Moreover, due to limited wetlands, the use of reserve land in that agroecological zone should be in accordance with its designation, comply with the priority principles, and be supported by government regulations and policies, so that food security can be maintained until 2045.

Despite Indonesia’s significant economic progress, certain regions, such as West Manggarai Regency in East Nusa Tenggara, continue to face persistent poverty challenges. While strategic tourism initiatives in Labuan Bajo have spurred regional development, the benefits have not reached local communities equitably, highlighting a disconnect between economic growth and community well-being. Addressing this gap requires an integrated approach that links social protection, disaster risk reduction, climate adaptation, and economic diversification. This paper proposes an adaptive social protection (ASP) framework that aims to increase the resilience of vulnerable populations by integrating social protection systems with disaster preparedness and sustainable economic strategies. The research critically examines the Regional Medium-Term Development Plan (RPJMD) of Kabupaten Manggarai Barat (2021–2026), identifying existing policy gaps and opportunities for improvement. Using a mixed-methods approach, this study used cluster mapping and geographically weighted regression analysis to model and visualise poverty distribution alongside infrastructure conditions. These findings will inform the design of a targeted ASP programme to reduce poverty and build resilience to economic and environmental shocks. By aligning with sustainable development principles, the proposed framework addresses the dual goals of poverty reduction and disaster risk reduction. This study provides actionable recommendations for local governments to strengthen social protection mechanisms, promote inclusive economic growth, and ensure equitable distribution of tourism benefits. The findings provide a policy blueprint for promoting sustainable and inclusive development in West Manggarai Regency, with implications for similar contexts in other regions.

The Indonesian land area is 191.1 million ha, part of which is abandoned land in various agroecosystems that have the potential for expanding the agricultural area. The purpose of this research was to geospatially analyze abandoned land based on its agroecosystem at the national and district levels, as well as to evaluate the land suitability of the land for expanding agricultural development. The methods included: (1) geospatial analysis of the national land cover map at a scale of 1:250,000 combined with soil and climate information to identify abandoned land and examine its agroecosystem, (2) selecting representative districts in each agroecosystem for visual interpretation using high-resolution imagery, i.e., SPOT 6/7, (3) assessing the land suitability of abandoned land for agricultural development at the national and district levels, and (4) predicting national abandoned land and its land suitability. The essential finding is the identification of abandoned land at around 42.6 million ha in Indonesia distributed over six agroecosystems, with the widest being in dry lowland and wet climates. Then, 54 districts were selected to characterize abandoned land by using SPOT 6/7 high-resolution imagery and were interpreted visually. It was found that the abandoned land covered approximately 16.9 million ha. The distribution of abandoned land from the interpretation of satellite imagery was smaller than that of geospatial analysis due to differences in the map scale and the use of ancillary data. The identification of abandoned land from high-resolution imagery should be carried out for all regions of Indonesia to accurately map the distribution of the abandoned land and characterize the properties. However, it requires a large amount of time, cost, and facilities to complete the inventory. The geospatial analysis that combined imageries and ancillary data identified 27.7 million ha of abandoned land suitable for expanding the agricultural area. The largest suitable abandoned land for the purpose was found in the lowlands with a wet climate, especially in Papua, Kalimantan, and Sumatra islands. The identified suitable abandoned land of 54 districts differed by scale, in which it was 11.2 million ha at the scale of 1:250,000 and 8.5 million ha at the scale of 1:50,000, respectively. The potential land expansion for food crops, particularly paddy fields, was only 2.2 million ha, located in mineral swamp land, which was predominantly located in Papua, with inadequate accessibility. Expanding paddy fields for national food security in the future would be constrained by less suitable land resources, while the near future challenge is the competition of land allocation for agricultural and non-agricultural sectors, as well as for food crops and plantations.

Rural poverty in Indonesia remains a complex issue involving various aspects. West Manggarai, East Nusa Tenggara, is a national tourist destination and a significant focus of national development, yet poverty rates remain very high. Therefore, this study developed a Composite Poverty Index (CPI) using the Sustainable Rural Livelihoods Approach (SRLA) to illustrate the complexity of rural deprivation in West Manggarai Regency. The CPI was developed by normalizing eighteen validated indicators across five livelihood capitals—human, social, natural, physical, and financial. These indicators were then classified using a Likert-type scale, and their weights were determined through the Analytic Hierarchy Process (AHP) to produce village-level CIP scores. The results show that most villages fall into the “Moderate” category (CIP: 0.40–0.60), reflecting chronic but not extreme deprivation. Spatial inequalities are evident, particularly in access to education, infrastructure, clean water, financial services, and ecological resources. Remote villages recorded higher CIP scores. Natural and economic capital were weakest, while human and social capital performed relatively well. Therefore, poverty alleviation in West Manggarai requires an integrated strategy tailored to local spatial conditions and livelihood capital.

Rural poverty in Indonesia remains a complex issue involving various aspects. West Manggarai, East Nusa Tenggara, is a national tourist destination and a significant focus of national development, yet poverty rates remain very high. Therefore, this study developed a Composite Poverty Index (CPI) using the Sustainable Rural Livelihoods Approach (SRLA) to illustrate the complexity of rural deprivation in West Manggarai Regency. The CPI was developed by normalizing eighteen validated indicators across five livelihood capitals—human, social, natural, physical, and financial. These indicators were then classified using a Likert-type scale, and their weights were determined through the Analytic Hierarchy Process (AHP) to produce village-level CIP scores. The results show that most villages fall into the "Moderate" category (CIP: 0.40–0.60), reflecting chronic but not extreme deprivation. Spatial inequalities are evident, particularly in access to education, infrastructure, clean water, financial services, and ecological resources. Remote villages recorded higher CIP scores. Natural and financial capital performed the weakest, while human and social capital performed relatively well. Therefore, poverty alleviation in West Manggarai requires an integrated strategy tailored to local spatial conditions and livelihood capital.

In 2020, during the early stages of the Covid-19 pandemic, Kendal Regency witnessed an increase in poverty rates as both national and global levels. Urban areas in Kendal Regency experienced a higher surge of 4.42 percent in low-income families compared to rural areas, which saw only a 0.43 percent increase. Suburbanization played a significant role due to Kendal Regency's proximity to Semarang City, the capital of Central Java Province. Interestingly, poverty-related issues were more prevalent in suburban areas. Consequently, a study was conducted to analyze poverty in urban, suburban, and rural areas in Kendal Regency. The research aimed to achieve two objectives: (1) establish spatial zoning in Kendal Regency based on the three categories, and (2) analyze the increase in poverty during the early period of the pandemic in each category. Spatial zoning was performed using the K-Means Clustering technique, while descriptive quantitative techniques and spatial analysis with the Moran Index and Local Indicators of Spatial Autocorrelation (LISA) were used for analysis. The results indicated that the Covid-19 pandemic affected the composition of poor households differently across urban, suburban, and rural areas. Additionally, the analysis revealed that poverty tended to cluster in suburban areas of Kendal Regency.Keywords: poverty, urban, suburban, rural, Covid-19AbstrakPada tahap awal pandemi Covid-19 di tahun 2020, Kabupaten Kendal mengalami peningkatan angka kemiskinan sebagaimana terjadi di lingkup nasional maupun global. Daerah perkotaan di Kabupaten Kendal mengalami lonjakan yang lebih tinggi sebesar 4,42 persen pada keluarga miskin dibandingkan dengan daerah perdesaan yang hanya mengalami peningkatan sebesar 0,43 persen. Suburbanisasi memainkan peran penting karena kedekatan Kabupaten Kendal dengan Kota Semarang, ibu kota Provinsi Jawa Tengah. Menariknya, isu-isu terkait kemiskinan lebih banyak terjadi di daerah suburban. Oleh karena itu, dilakukan penelitian untuk menganalisis kemiskinan di perkotaan, suburban, dan perdesaan di Kabupaten Kendal. Penelitian ini bertujuan untuk mencapai dua tujuan: (1) menetapkan zonasi tata ruang di Kabupaten Kendal berdasarkan ketiga kategori tersebut, dan (2) menganalisis peningkatan kemiskinan pada periode awal pandemi di setiap kategori. Zonasi spasial dilakukan dengan menggunakan teknik K-Means Clustering, sedangkan teknik deskriptif kuantitatif dan analisis spasial dengan Moran Index dan Local Indicators of Spatial Autocorrelation (LISA) digunakan untuk analisis. Hasilnya menunjukkan bahwa pandemi Covid-19 memengaruhi komposisi rumah tangga miskin secara berbeda di perkotaan, suburban, dan perdesaan. Selain itu, analisis mengungkapkan bahwa kemiskinan cenderung mengelompok di daerah suburban Kabupaten Kendal.Kata kunci: kemiskinan, perkotaan, suburban, perdesaan, Covid-19