Food and Energy Security最新文献

Employing machine learning models on preprocessed samples is an effective alternative for leaf nitrogen quantification, reducing analysis time and improving fertilizer efficiency. This study evaluates predictive performance and transfer learning of models for nitrogen (N) concentration across different plant species, along with visual analysis of spectral patterns. A spectral dataset was developed using pre-processed samples from crops (coffee, pear, sugarcane, bean, and maize), forage (Brachiaria), and ornamental plants (e.g., Gypsophila). Leaf samples were collected from field-grown plants, oven-dried at 60°C with forced air circulation, and ground to 2.0 mm. Spectra were measured with a FieldSpec spectroradiometer (350–2500 nm). Visual analysis compared plants of distinct photosynthetic cycles (C3 and C4) and among species of the same cycle. Nitrogen quantification was performed using Partial Least Squares Regression (PLSR) and Random Forest (RF). Transfer learning was assessed in three ways: (i) between species; (ii) temporal stability; (iii) evaluation with an independent dataset comprising multiple agricultural species. Results showed C3 plants had lower reflectance in the 400–670 nm bands and higher N levels compared to C4 crops. Regardless of crop type or photosynthetic cycle, characteristic absorption features were detected at 530 nm and 615 nm, absent in fresh samples. PLSR achieved superior performance (R² = 0.95, RMSE = 2.16 g kg⁻¹, MAPE = 10.70%) compared to RF (R² = 0.88, RMSE = 3.4 g kg⁻¹, MAPE = 13.48%). Edaphoclimatic and physiological conditions influenced transfer learning, highlighting the potential and limitations of applying spectral models across species and environments.

Employing machine learning models on preprocessed samples is an effective alternative for leaf nitrogen quantification, reducing analysis time and improving fertilizer efficiency. This study evaluates predictive performance and transfer learning of models for nitrogen (N) concentration across different plant species, along with visual analysis of spectral patterns. A spectral dataset was developed using pre-processed samples from crops (coffee, pear, sugarcane, bean, and maize), forage (Brachiaria), and ornamental plants (e.g., Gypsophila). Leaf samples were collected from field-grown plants, oven-dried at 60°C with forced air circulation, and ground to 2.0 mm. Spectra were measured with a FieldSpec spectroradiometer (350–2500 nm). Visual analysis compared plants of distinct photosynthetic cycles (C3 and C4) and among species of the same cycle. Nitrogen quantification was performed using Partial Least Squares Regression (PLSR) and Random Forest (RF). Transfer learning was assessed in three ways: (i) between species; (ii) temporal stability; (iii) evaluation with an independent dataset comprising multiple agricultural species. Results showed C3 plants had lower reflectance in the 400–670 nm bands and higher N levels compared to C4 crops. Regardless of crop type or photosynthetic cycle, characteristic absorption features were detected at 530 nm and 615 nm, absent in fresh samples. PLSR achieved superior performance (R² = 0.95, RMSE = 2.16 g kg⁻¹, MAPE = 10.70%) compared to RF (R² = 0.88, RMSE = 3.4 g kg⁻¹, MAPE = 13.48%). Edaphoclimatic and physiological conditions influenced transfer learning, highlighting the potential and limitations of applying spectral models across species and environments.

Maize is a staple crop critical for food security and livelihoods of smallholder farmers in Cameroon. However, productivity is constrained by socio-economic, agricultural, institutional and climatic factors. This study characterises maize farming systems across four agroecological zones (AEZs: Bimodal Rainfall Humid Forest, Western Highlands, Monomodal Rainfall Humid Forest and Sudano-Sahelian) and identifies key drivers of yield using data from 303 farming households collected via semi-structured questionnaires. Analyses employed descriptive statistics, Welch-ANOVA and ANCOVA. Significant zonal differences emerged in farmer demographics, including gender, education, household size, experience and income. Institutional access also varied, with extension services and credit access highest in the Sudano-Sahelian zone (89.4% and 44.7% respectively), and market access highest in the Bimodal Rainfall Humid Forest zone (93.1%). Farm sizes ranged from 0.7 to 2.1 ha and yields from 0.9 to 3.2 t/ha. Most farmers preferred local varieties, with improved variety adoption rates varying from 10.6% to 47.7%. The growing season was longest in the Sudano-Sahelian zone (21.2 weeks). Farmers in this zone avoided intercropping, while fertiliser and pesticide use was lowest in the Monomodal Rainfall Humid Forest zone (< 41%). Storage methods included polypropylene bags in the Bimodal Rainfall Humid Forest and Sudano-Sahelian zones, and traditional granaries in the Western Highlands. ANCOVA explained 61.4% of yield variance and identified farm size, credit access and economic status as significant (p < 0.05 to 0.001) positive drivers. Access to extension services showed a marginal positive influence on productivity, while household labour and Sudano-Sahelian zone had marginal negative effects (p < 0.1), with the latter likely reflecting the challenges posed by harsh arid climatic conditions. Findings highlight zone-specific challenges and opportunities, emphasising the need for targeted interventions, such as improved credit, extension services and climate-resilient practices, to enhance maize productivity and food security in Cameroon and similar Central African contexts.

Maize is a staple crop critical for food security and livelihoods of smallholder farmers in Cameroon. However, productivity is constrained by socio-economic, agricultural, institutional and climatic factors. This study characterises maize farming systems across four agroecological zones (AEZs: Bimodal Rainfall Humid Forest, Western Highlands, Monomodal Rainfall Humid Forest and Sudano-Sahelian) and identifies key drivers of yield using data from 303 farming households collected via semi-structured questionnaires. Analyses employed descriptive statistics, Welch-ANOVA and ANCOVA. Significant zonal differences emerged in farmer demographics, including gender, education, household size, experience and income. Institutional access also varied, with extension services and credit access highest in the Sudano-Sahelian zone (89.4% and 44.7% respectively), and market access highest in the Bimodal Rainfall Humid Forest zone (93.1%). Farm sizes ranged from 0.7 to 2.1 ha and yields from 0.9 to 3.2 t/ha. Most farmers preferred local varieties, with improved variety adoption rates varying from 10.6% to 47.7%. The growing season was longest in the Sudano-Sahelian zone (21.2 weeks). Farmers in this zone avoided intercropping, while fertiliser and pesticide use was lowest in the Monomodal Rainfall Humid Forest zone (< 41%). Storage methods included polypropylene bags in the Bimodal Rainfall Humid Forest and Sudano-Sahelian zones, and traditional granaries in the Western Highlands. ANCOVA explained 61.4% of yield variance and identified farm size, credit access and economic status as significant (p < 0.05 to 0.001) positive drivers. Access to extension services showed a marginal positive influence on productivity, while household labour and Sudano-Sahelian zone had marginal negative effects (p < 0.1), with the latter likely reflecting the challenges posed by harsh arid climatic conditions. Findings highlight zone-specific challenges and opportunities, emphasising the need for targeted interventions, such as improved credit, extension services and climate-resilient practices, to enhance maize productivity and food security in Cameroon and similar Central African contexts.

Rice (Oryza sativa L.) is an essential food crop, usually grown in flooded soils. However, these environments, especially with low pH, favor iron (Fe) toxicity due to the low redox potential, which increases the Fe²⁺ availability. Excessive Fe concentrations are highly detrimental, compromising the growth, physiology, and productivity of rice plants. In this context, dopamine has emerged as a bioactive molecule with the potential to mitigate stresses in plants. Therefore, the objective of this study was to evaluate whether the exogenous application of dopamine attenuates oxidative damage in the photosynthetic apparatus of rice leaves subjected to excess Fe, as well as to analyze anatomical changes, production of reactive oxygen species (ROS), activity of antioxidant enzymes, and the nutritional status of plants. Fe excess caused the accumulation of this element in roots and leaves, reducing the uptake of other essential nutrients. However, the application of dopamine significantly increased the nutritional status while reducing the accumulation of Fe in plants. In the anatomy, dopamine promoted improvements in root structures, primarily in the thickness of the root epidermis (21%), as well as enhancements in leaves, including an increase in chlorophyll parenchyma (11%). Exogenous dopamine also minimized damage to the photosynthetic apparatus, increasing the levels of photosynthetic pigments and significantly increasing the effective quantum yield of PSII photochemistry (13%) and electron transport rate (13%). In gas exchange, the dopamine application in plants under Fe excess promoted increases in the net photosynthetic rate and water use efficiency with increases of 14% and 25%, respectively. The antioxidant defense was intensified by dopamine, with increases in the activities of superoxide dismutase (33%), catalase (29%), ascorbate peroxidase (75%) and peroxidase (17%). In parallel, there was a reduction in the ROS accumulation, including superoxide (14%) and hydrogen peroxide (8%), as well as malondialdehyde (37%) and electrolyte leakage (4%). Finally, the biomass was negatively impacted by excess Fe; however, dopamine promoted increases in stem and root growth, proving its effectiveness in mitigating the toxic effects of Fe in rice.

Seed pelleting is an emerging precision-agriculture technology that transforms small or irregular seeds into uniform units to enhance mechanical sowing, placement accuracy, and early crop establishment. Pelleting performance depends on the interplay among binder–filler composition, pellet structure, and post-pelleting moisture conditions, which collectively influence durability, germination, and seedling vigor. Recent developments include biodegradable and bio-based materials, biochar and micronutrient additives, and biological agents that enhance stress tolerance and early growth. Advances in pelleting machinery and quality-control tools have improved uniformity and process automation, while nano-enabled and stimuli-responsive coatings introduce new opportunities for controlled release and climate-resilient applications. Integrating mechanistic insights on filler–binder interactions with digital technologies such as artificial intelligence (AI) offers a pathway toward more consistent and scalable formulations. Despite these gains, adoption remains limited in smallholder systems due to cost, access, and material constraints. Seed pelleting represents a converging frontier of material science, engineering, and sustainable agriculture, with significant potential to improve input efficiency and contribute to resilient food systems.

This study investigated the post-COVID-19 food security and livelihood status of the marginalized Garo indigenous community in Bangladesh. A cross-sectional household survey was conducted from August to November 2023, encompassing 300 households in the Tangail and Mymensingh districts of Bangladesh. The household food insecurity access scale (HFIAS) and a livelihood assessment index (LAI) were utilized in conjunction with a logistic regression model to ascertain the determinants of household food security. The findings revealed that food insecurity, which was prevalent in 93% of households during lockdown, improved to 59% after the pandemic. Financial capital and natural capital were most significantly impacted, whereas physical capital remained relatively stable. The regression analysis indicated that increased household income, natural capital, and physical capital are positively and significantly correlated with food security status. Common coping strategies, which include reducing meal size and frequency and consuming fewer preferred foods, were identified. The findings also suggest that despite ongoing recovery, persistent structural vulnerabilities necessitate policy interventions, including income support, targeted credit, improved agricultural inputs, and strengthened social safety nets, to enhance resilience and mitigate reliance on negative coping mechanisms within indigenous households.

Unreasonable nitrogen fertilizer use intensifies nitrogen losses, resulting in environmental contamination. Nitrification inhibitors and urease inhibitors are widely employed to reduce environmental pollution and enhance nitrogen use efficiency. However, under fertigation, the mechanism by which nitrogen cycle inhibitors improve dry matter accumulation and yield in summer maize by influencing physiological processes remains unclear. Consequently, a field experiment was conducted from 2020 to 2021 in the Huang-Huai-Hai region using a fertigation management system. The experiment included five treatments: no nitrogen application (N0), urea ammonium nitrate alone (U), urea ammonium nitrate with both urease and nitrification inhibitors (U-DN), urea ammonium nitrate with a urease inhibitor (U-N), and urea ammonium nitrate with a nitrification inhibitor (U-D). Nitrogen was applied at a rate of 210 kg ha⁻¹, with nitrogen inhibitors added at 0.05% of the total nitrogen input. The study systematically evaluated the effects of different inhibitor applications on summer maize photosynthetic characteristics, antioxidant capacity, and grain yield. The results showed that, under fertigation, the combined application of nitrogen cycle inhibitors with urea ammonium nitrate optimized nitrogen supply during the crop's later growth stages. This enhanced leaf antioxidant enzyme activity, reduced malondialdehyde content by 19.7%, effectively delayed leaf senescence, enhanced photosynthetic potential and net assimilation rate during the grain filling stage, and promoted dry matter accumulation in the late growth stage, ultimately increasing summer maize yield by 13.2%. Overall, these findings indicated that nitrogen cycle inhibitors optimize the spatiotemporal effectiveness of nitrogen supply under fertigation, thereby enhancing late-stage photosynthetic performance and dry matter accumulation through delayed leaf senescence, and provide practical insights for achieving high and stable maize yields through optimized nitrogen management.

Mitigating climate change while guaranteeing food security is an important issue. Currently, the coordination of food security, carbon reduction and sequestration (CRS) and its influencing mechanism are unclear. In this study, we analyze the interactions and influencing factors between the two using a geographically and temporally weighted regression (GTWR) model. The results show that the national coupling coordination degree (CCD) increased from 0.477 in 2001 to 0.508 in 2022, indicating a shift from dissonance to coordination. Significant regional heterogeneity exists in coupling coordination. The GTWR results reveal that the share of grain-sown area, agricultural technology, labor quantity and quality, and mechanization exert significant positive effects on coordinated development, whereas chemical fertilizer use has a significant negative impact. Moreover, a U-shaped relationship is identified between regional economic development and CCD, suggesting that economic growth initially constrains but eventually promotes coordinated development after surpassing a certain threshold. These findings highlight the need for region-specific policy design, with a particular emphasis on improving education and human capital in western and southwestern China, as well as promoting the diffusion and application of agricultural technologies and mechanization.

Iron (Fe) is an essential micronutrient for plant photosynthesis and human health. Pear represents a widely consumed fruit for human Fe intake, yet its yield and quality are frequently challenged by Fe deficiency (FD) stress. Despite the prevalence of FD stress in agricultural production under generally alkaline and calcareous conditions, pear plants implement a series of adaptive responses to maintain Fe homeostasis, which remains poorly understood. In this study, key time points for RNA-seq analysis were determined by examining FD-related physiological indicators in pear seedlings (Pyrus betulaefolia) under short-term FD stress. The results revealed that FD stress enhanced root rhizosphere acidification (peaking at 24 h post-treatment) and caused a gradual decrease in leaf SPAD value and Fe content, while no obvious aboveground chlorosis phenotype was observed. By comparing RNA-seq data of root samples at 3, 6, 12, and 24 h post-FD stress with the control (0 h), a total of 8369 differentially expressed genes (DEGs) were generated, and 1423 DEGs were identified throughout the stress period. Functional annotation indicated that DEGs were enriched in transcriptional regulation, signal transduction, and secondary metabolism, while KEGG enrichment implied that DEGs are involved in sugar, proline, γ-aminobutyric acid (GABA), galactose, raffinose, and polyamines metabolism, as well as hormone signaling. In addition, 18 PbHAs, 18 PbFROs, and 19 PbIRTs were identified, where Chr13.g22071 (PbHA), Chr7.g31823 (PbFRO), and Chr11.g10287 and Chr11.g10606 (PbIRTs) may be responsible for Fe homeostasis in FD-stressed pear plants. Moreover, 490 transcription factors (TFs) were screened from the DEGs, with ERF, MYB, WRKY, bHLH, and NAC TFs accounting for the majority. Notably, 21 from 36 bHLHs were FD-induced, among which Chr3.g19682, Chr5.g08031, Chr2.g44023, and Chr8.g558833 might be the core FD regulators. Furthermore, based on the results of the gene coexpression analysis, an intricate regulatory network showing synergistic or antagonistic interactions between these TFs and core Fe uptake-related genes has been established. Overall, this study identifies prospective genes for maintaining Fe homeostasis under FD stress, offering a theoretical foundation for further research into the molecular mechanisms of pear adaptation to FD stress, and potentially guiding the development of FD-tolerant pear varieties.