Ministry of Agriculture and Cooperatives

Ensuring a sustainable source of nutritious food is critical for long-duration space missions. Thai landrace rice 466HM exhibits high nutritional value and stress resilience, making it a promising candidate for space cultivation, yet its response to low Earth orbit (LEO) conditions remains poorly understood. This study compared rice grains maintained under terrestrial conditions with grains stored aboard the Shijian-19 (SJ-19) reusable satellite, orbiting at ~336 km for 13.5 days under microgravity (2-7 × 10-7g) and an absorbed radiation dose of ~0.153 rad (Si). Volatile compound profiling, texture analysis of cooked grains, and simulated gastrointestinal digestion followed by peptide mass fingerprinting were performed. LEO-exposed rice grains exhibited a 1.67-fold increase in adhesiveness compared to Earth-based rice (p < 0.01), while hardness remained unchanged between the two groups (p > 0.05), alongside distinct alterations in flavor-related volatile compounds and peptide profiles. Principal component analysis revealed clear separation between Earth and LEO-exposed samples, indicating microgravity-associated shifts in digestible peptide composition. Cytotoxicity assessment using MTT assays in HT-29 and HepG2 cells confirmed the safety of both rice types. These findings demonstrate that orbital conditions influence the compositional, functional, and sensory attributes of rice, providing insights relevant to space agriculture and astronaut nutrition.

ABSTRACT Since the UN Sustainable Development Goals (SDGs) were adopted in 2015, many countries have pledged to pursue sustainable development, yet a phenomenon of “cherry‐picking” has emerged, whereby governments selectively emphasize goals and targets that fit their interests. This study provides empirical evidence of cherry‐picking among Asian countries by applying latent semantic analysis (LSA) to SDG‐related texts and using the resulting similarity scores in a cluster analysis. We identify six clusters of countries: four comprising cherry‐picker countries, one comprising follower countries that largely follow the UN SDG framework, and one unclassified cluster. Because such selectivity can undermine the integrated nature of the SDGs, we propose a two‐step policy approach: short‐term reforms that strengthen SDG evaluation and bring cherry‐picking and follower behavior explicitly into public debate, and longer‐term revisions of the global SDG framework beyond 2030 so that future agendas incorporate preparedness for unforeseen global shocks and better protect food security.

This study examines human capital development and economic growth nexus in Nigeria using ex post facto research design. The study applied an econometric regression technique of the Ordinary Least Square (OLS) to ascertain the effect of macroeconomic growth induced variables on human capital development in Nigeria. Our findings from the cointegrating regression result test suggest that there is strong evidence of cointegration between dependent variable (HCD) and the independent Variables. Thestudy revealed a long-run causal relationship between dependent variable (HCD) and the independent Variables. It could be observed from our findings that the relationship between some of the variables like TGE, TEE, THE, and FDI and human capital development is positive while that of LEI is negative. This means that TGE, TEE, THE, and FDI have direct relationships with human capital development. In other words, an increase in TGE, TEE, THE, and FDI will results to a rise in human capital development whereas an increase in LEI will probably result in a fall in human capital development. In addition, the results show that TGE, TEE, THE, and FDI have statistical significance on human capital development in Nigeria while LEI is statistically insignificance on human capital development. This study therefore recommends that the government should give educational grants, provide vocational training, provide basic health facilities; enhance the competitiveness of the economy. This will avail the country the enhanced entrepreneurial creativity, a suitable, competent, healthy and educated labor force to contribute meaningfully to national development. The government should make adequate budget for education in line with the UNESCO recommendation. This will help facilitate proper administration of financial revenues and other school resources. There is the need for increased government funding for health care. They will help reduce the challenges in healthcare and the increasing medical tourism outside the country and consequently enhance the countries life expectancy. The government should create a secured and business friendly environment to help attract FDI in the country.

The implementation of the framework offered by the international classification of functioning disability and health (ICF) is recognized as a necessary step in the process of school inclusion for pupils with disabilities. This step may be greatly eased by informatics tools that guide users without requiring extensive familiarity with the ICF. Here we describe the development and testing of PETRA, a specific digital tool that supports the personalization of the educational path for students with certified disabilities in Italy. The tool foresees the active participation of the family and the student himself in his own training path and guides the user through sequential steps: observation, planning, realization, validation, The usability and efficiency of the tool was tested in a representative set of schools in the Trento province. Using this tool, each student is considered in his uniqueness and the educational and didactic interventions are calibrated to its specificities (including the oftenunrecognized potentialities,) and the context in which it lives. The tool was well received, showed its efficiency in each of the steps promising to offer substantial support in improving school inclusion and empowerment of students with disabilities.

Using annual series that span the period from1981-2022 and employing the ARDL framework, this study examines if FDI inflows lead to domestic currency appreciation in Nigeria. Finding indicates that in the short-run and the long-run, FDI inflows impacted negatively and significantly on exchange rate. Also, one period lag of interest rate impacted positively on exchange rate in the short-run but in the long-run the impact was negative. Net barter terms of trade also impacted exchange rate positively. The significant impact of FDI inflows on exchange rate provides an alternative view of the link between the two variables different from the postulation of Aliber’s hypothesis. Consequently, policymakers should weigh the benefits and costs of FDI inflows bearing in mind that continuous appreciation of the domestic currency occasioned by FDI inflows could adversely affect the terms of trade position.

農作業の負担増加は，農業者数の減少や農業者の高齢化などの深刻な課題の一つとして挙げられる．これらの課題を解決する手段として，北海道十勝地域の畑作経営においてはスマート農業技術の導入が進められている．本研究では，スマート農業技術の導入実態を調査するとともに，農作業負担軽減効果の評価を行った．その結果，スマート農業技術導入前と比較して，身体的および精神的疲労の程度が約40%軽減されることが確認された．特に，自動操縦システムは顕著な効果を示しており，今後さらに導入が進展すると予測される．加えて，セクションコントロール，可変施肥，農薬散布用ドローン，ロボットトラクタなどのスマート農業技術への期待も高まっている．

Abstract. Soil bulk density (BD), a key physical property affecting soil compaction, porosity, and carbon stock estimation, exhibits considerable spatial and temporal variability. However, current BD estimation methods especially traditional pedotransfer functions (PTFs) are inherently static and not designed for temporal analysis. This presents a significant limitation for soil monitoring across large and heterogeneous regions. In this study, we developed a machine learning (ML) approach integrated with remote sensing data to map and monitor BD across Thailand from 2004 to 2009 at national scale. We used multispectral indices, topographic variables, climate data, and organic carbon content to train six ML models: Artificial Neural Networks (ANN), Deep Neural Networks, Random Forest, Support Vector Regression, XGBoost, and LightGBM. Model performance was evaluated using in-situ BD measurements from 236 soil samples collected in 2004. For benchmarking purposes, 76 published PTFs were also assessed on the same dataset. Results showed that the ANN model achieved the highest prediction accuracy (R2 = 0.986; RMSE = 0.017 g cm-3), outperforming both other ML models and all PTFs. Temporal analysis using the ANN model revealed a 7.27 % increase in mean BD and a 41.23 % reduction in standard deviation between 2004 and 2009, indicating increased soil compaction and reduced variability. Feature importance analysis identified organic carbon, vegetation indices, slope, and temperature as the most influential variables. The resulting high-resolution BD maps captured national-scale spatial and temporal trends and provide a robust foundation for soil quality monitoring, carbon accounting, and sustainable land use planning in tropical agroecosystems.