Search Results (20)

This paper estimates the direct and indirect effects of agriculture on health and nutrition using nationally representative survey data collected by the Kenya National Bureau of Statistics in 1994, 1997, 2005 and 2015. The models estimated serve as examples of general frameworks that can be used to measure the direct and indirect effects of agriculture on health and nutrition in Africa and elsewhere. The results indicate that substantial direct and indirect improvements in health and nutrition can be achieved via policies that increase agricultural productivity. Growth in household income is the main mechanism through which the effects are transmitted to household members. Exogenous variation in household holding of land and cattle is used to identify the effects we estimate. The idea underlying the identification strategy is that agricultural policies over which households have no control can be used by the government to vary farm assets, thus changing household income, ceteris paribus. Examples of such policies include interventions that improve land tenure systems and agricultural extension services at the farm level. The conclusion highlights the policy value of our findings. Full article

(1) Background: Methyl jasmonate-treated D. morbifera adventitious root extract (MeJA-DMARE), enriched with phenolics, has enhanced bioactivities. However, phenolics possess low stability and bioavailability. Substantial evidence indicates that plant extract–phospholipid complex assemblies, known as phytosomes, represent an innovative drug delivery system. (2) Methods: The phytosome complex was created by combining MeJA-DMARE with Soy-L-α-phosphatidylcholine (PC) using three different ratios through two distinct methods (co-solvency method: A1, A2, and A3; thin-layer film method: B1, B2, and B3). (3) Results: Initial evaluation based on UV-Vis, entrapment efficiency (EE%), and loading content (LC%) indicated that B2 exhibited the highest EE% (79.98 ± 1.45) and LC% (69.17 ± 0.14). The phytosome displayed a spherical morphology with a particle size of 210 nm, a notably low polydispersity index of 0.16, and a superior zeta potential value at −25.19 mV. The synthesized phytosome exhibited superior anti-inflammatory activities by inhibiting NO and ROS production (reduced to 8.9% and 55.1% at 250 μg/mL) in RAW cells and adjusting the expression of related inflammatory cytokines; they also slowed lung tumor cell migration (only 2.3% of A549 cells migrated after treatment with phytosomes at 250 μg/mL), promoting ROS generation in A549 cell lines (123.7% compared to control) and stimulating apoptosis of lung cancer-related genes. (4) Conclusions: In conclusion, the MeJA-DMARE phytosome offers stable, economically efficient, and environmentally friendly nanoparticles with superior inflammation and lung tumor inhibition properties. Thus, the MeJA-DMARE phytosome holds promise as an applicable and favorable creation for drug delivery and lung cancer treatment. Full article

The present study evaluates the feasibility of using a fully suspended inter-array cable system for an offshore wind farm. It includes both numerical simulations and a scaled-down experiment, conducted at a 1:49 scale, to validate the numerical results. To achieve the goal, a 15 MW floating offshore wind turbine (FOWT) and a floating offshore substation (FOSS) are involved to simulate the wind farm array. This study incorporates the 50-year return period conditions of the Taiwan Hsinchu offshore area, which has a water depth of about 100 m, to validate the specifications related to the platform motion and mooring line tension. Additionally, an analysis of the tension, curvature, and fatigue damage of the dynamic cable system is discussed in this research. Because a fully suspended cable is a relatively new concept and may be more frequently considered in a deeper water depth area, numerical simulation software Orcina Orcaflex 11.4 has been chosen to conduct the fully coupled simulation, determining whether the fully suspended cable system could effectively withstand the challenges posed by extreme sea conditions. This is due to the reason that a fully suspended cable would occupy a larger space in the ocean, which may pose a risk by influencing the navigation of the vessels. Therefore, the cable laying depth under normal sea states is also discussed to evaluate the influence over vessel navigation. This study also collects the long-term environmental data from the Central Weather Bureau, Taiwan, to calculate the accumulative cable fatigue damage under different sea states. To integrate the results, this research applies fitness parameters to evaluate the feasibility of each cable configuration. Covering the cable performance under extreme sea states and regular operating sea states offers valuable insights for applications in ocean engineering. Full article

COVID-19, the most recent multi-dimensional global food crisis, challenged leadership and impacted individuals’ personal networks. Two cross-sectional surveys were disseminated to women involved in their state’s women’s leadership committee to understand food waste behaviors. An egocentric network analysis was chosen as the methodology to better understand personal advice network characteristics and examine the impacts of Farm Bureau women’s leadership committee members’ advice networks on their food waste behavior. A multilevel model was conducted to identify factors related to respondents leading their network members toward positive food waste decisions. Independent variables included in the variables at the individual (e.g., each respondent’s race, generation), dyadic (e.g., length respondent has known each member of her network), and network levels (e.g., proportion of the respondent’s network that was female) were included in the model. Women were more likely to report connections with people they led to positive food waste behaviors and food security when: they had higher food waste sum scores, they were part of Generation X, the network member they led to more positive food waste behaviors was a friend, and if there were fewer women in their advice networks. Full article

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R² values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm². Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm², respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health. Full article

Due to the rapid development of computers, researchers have made efforts since the 1990s to develop typhoon forecasting models and stochastic typhoon simulation models to assess typhoon disasters and risks. Typhoon forecasting models are primarily used to predict and track the movement of typhoons and provide warning information to the general public before landfall. Stochastic typhoon simulation models can assess extreme wind speeds and compensate for the limitations of current observations and simulation data length. Taiwan experiences approximately three to four typhoons yearly, of varying intensities and paths. Whether the marine meteorological data includes events of strong typhoon centers passing through will affect the results of frequency analysis. The development of offshore wind power in Taiwan is closely related to the unique marine meteorological conditions throughout the lifecycle stages, including wind farm site selection, feasibility studies, planning and design, construction and installation, operation and maintenance, and decommissioning. This study references relevant research and analyzes sixty-three scenarios using nine types of maximum storm wind speed radii and seven Holland-B parameters. The data from Japan Meteorological Agency Best Track Data (JMA BTD) is utilized, explicitly selecting 20 typhoon events after 2000 for wind speed simulation using a typhoon wind speed model. After validating the typhoon wind speeds with observation data from the Central Weather Bureau (CWB) in Hsinchu and the Longdong buoy, the technique of Monte Carlo simulation is utilized to generate synthetic typhoons randomly. The average of the relative absolute errors for the simulated maximum wind speeds is calculated, and through comprehensive evaluation, optimal parameter combinations ($R_m$, $B$) are obtained. Full article

This research discusses the viability of the next-generation novel materials, e.g., titanium alloy bars (TiABs) and ultra-high-performance concrete (UHPC) that have potential to be utilized in civil infrastructures, e.g., bridges, in combination with machine learning (ML) techniques. Since UHPC and TiABs have been demonstrated to be a realistic alternative to traditional construction materials for civil infrastructures, it is important to characterize bond performance of reinforcing, i.e., TiABs embedded in UHPC. The research utilizes improvement of ML techniques, e.g., transfer learning (TL) to predict the bond strength of TiABs in UHPC. Full article

The spatial distribution of cotton fields is primary information for national farm management, the agricultural economy and the textile industry. Therefore, accurate cotton information at the regional scale is required with a rapid increase due to the chance provided by the huge amounts of satellite images accumulated in recent decades. Research has started to introduce the phenology characteristics shown at special growth phases of cotton but frequently focuses on limited vegetation indices with less consideration on the whole growth period. In this paper, we investigated a set of phenological and time-series features with optimization depending on each feature permutation’s importance and redundancy, followed by its performance evaluation through the cotton extraction using the Random Forest (RF) classifier. Three sets of 31 features are involved: (1) phenological features were determined by the biophysical and biochemical characteristics in the spectral space of cotton during each of its five distinctive phenological stages, which were identified from 2307 representative cotton samples using 21,237 Sentinel-2 images; (2) three typical vegetation indices were functionalized into time-series features by harmonic analysis; (3) three terrain factors were derived from the digital elevation model. Our analysis of feature determination revealed that the most valuable discriminators for cotton involve the boll opening stage and harmonic coefficients. Moreover, both qualitative and quantitative validation were performed to evaluate the retrieval of the optimized features-based cotton information. Visual examination of the map exhibited high spatial consistency and accurate delineation of the cotton field. Quantitative comparison indicates that classification of RF-coupled optimized features achieves improved overall accuracy 5.53% higher than that which works with either the limited vegetation indices. Compared with all 31 features, the optimized features realized greater identification accuracy while using only about half the number of features. Compared with test samples, the cotton map achieved an overall accuracy greater than 98% and a kappa more than 0.96. Further comparison of the cotton map area at the county-level showed a high level of consistency with the National Bureau of Statistics data from 2020, with R² over 0.96, RMSE no more than 14.62 Kha and RRMSE less than 17.78%. Full article

Collective Forestland Tenure Reform has confirmed the forestland tenure of rural households and made forestland property rights clearer. In order to explain whether this policy is effective in improving rural households’ expected returns and sense of forestland tenure security, we built models to study the impact of off-farm employment on forestland input in the context of labor migration to urban areas. We used data from the rural household tracking survey conducted by the Development Research Center of the National Forestry and Grassland Bureau from 2003–2016, which includes nine provinces (districts) and 1227 sample rural households in China. Regression models with the forestland titling program as the key influencing factor were constructed, controlling for household characteristics, household head characteristics, forestland characteristics, village level characteristics, market characteristics, and policy factors. Forestland leases had no significant on cash outlays and labor inputs. Forest tenure mortgage loans had a significant positive effect on cash outlays and labor inputs. For households’ off-farm employment, the moderating effects of labor migration on labor inputs and cash outlays are modeled separately. The study indicated that the forestland tenure titling certificates increase households’ enthusiasm in forestland production and promote cash outlays and labor inputs in forestland management. The results regarding the moderating effect indicated that labor migration has a positive moderating effect on rural households’ forestland inputs including labor inputs and cash outlays. Full article

Poverty remains a major problem among refugees, and the COVID-19 pandemic seems to have exacerbated its incidences. In Kenya, although refugees ordinarily face serious economic conditions, COVID-19 worsened their economic status. The objective of this paper was to analyze the determinants of poverty dynamics among Kenyan refugees during the COVID-19 pandemic. The data were the COVID-19 rapid response panel data that were collected between May 2020 and June 2021 by the Kenyan National Bureau of Statistics (KNBS), the United Nations High Commissioner for Refugees (UNHCR) and the University of California, Berkeley with technical assistance from the World Bank. The random effects probit regression model was used for data analysis using the absolute and relative poverty lines. The results showed that, using the Kenya’s national poverty lines, 73.03% of the respondents were poor across time, while there was a steady decline in poverty incidences from 76.55 in July–September 2020 to 68.44% in March–June 2021. The results further showed the presence of significant heterogeneity, thereby justifying the panel estimation approach. Poverty significantly declined (p < 0.05) with receipt of food assistance, remittances, gifts, amount of loan, amount realized from sale of assets and agricultural enterprises, while it increased with education, household size, non-farm enterprises, residence in urban areas, and at the Kakuma, Kalobeyei and Shona camps. It was concluded that welfare deprivation among refugees during COVID-19 is pathetic, and post-COVID-19 recovery should, among other things, take cognizance of place and camp of residence, and access to some form of socioeconomic support. Full article

Individual trees are characterized by various sizes and forms, such as diameter at breast height, total height (H), height to crown base (HCB), crown length (CL), crown width, and crown and stem forms. Tree characteristics are strongly related to each other, and studying their relationships is very important. The knowledge of the compatibility and additivity properties of the major tree characteristics, such as H, CL, and HCB, is essential for informed decision-making in forestry. H can be used to represent site quality and CL represents biomass and photosynthesis of crown, which is the performance of individual tree vigor and light interception, and the longer the crown length (or shorter HCB) is, the more vigorous the tree would be. However, none of the studies have uncovered their inherent relationships quantitatively. This study attempts to explore such relationships through the application of appropriate modeling approaches. We applied seemingly unrelated regression, such as nonlinear seemingly unrelated regression (NSUR), which is commonly used for exploring the compatibility and additivity properties of the variables, for the proposes. The NSUR involves the variance and covariance matrices of the sub-models that are used for the interpretation of the correlations among the variables of interest. The data set acquired from Mongolian oak forest and spruce-fir forest in the Jingouling forest farm of the Wangqing Forest Bureau in the Northeast of China were used to construct two types of model systems: a compatible model system (the model system of H, CL, and HCB can be estimated simultaneously) and an additive model system (the sum of HCB and CL is H, the form of the H sub-model equals the sum of the HCB and CL sub-models) from the individual models of H, CL, and HCB. Among the various tree-level and stand-level variables evaluated, D (diameter at breast), Dg (quadratic mean diameter), DT (dominant diameter), CW (crown width), SDI (stand density index), and BAS (basal area of stand) contributed significantly highly to the variations of the response of the variables of interest in the model systems. Modeling results showed the existence of the compatibility and additivity of H, CL, and HCB simultaneously. The additive model system exhibited better fitting performance on H and HCB but poorer fitting on CL compared with the simultaneous model system, indicating that the performance of the additive model system could be higher than that of the simultaneous model system. Model tests against the validation data set also confirmed such results. This study contributes a novel approach to solving the compatibility and additivity of the problems of H, CL, and HCB models through the application of the robust estimating method, NSUR. The results and algorithm presented will be useful for constructing similar compatible and additive model systems of multiple tree-level models for other tree species. Full article

Tree height is a basic input variable in various forest models, such as growth and yield models, biomass models, and carbon budget models, which serve as very important tools for the informed decision-making in forestry. The height-diameter model is the most important component of the growth and yield models and forest simulators. We developed the nonlinear mixed-effects height-diameter model with the interaction effects of stand density and site index introduced using data from 765 Larix olgensis trees in Jingouling forest farm of the Wangqing Forest Bureau in northeast China. Among the various basic versatile functions evaluated, a simple exponential growth function fitted the data adequately well, and this was then expanded through the introduction of the variables describing the interaction effects of the stand density and site index on the height-diameter relationship. Sample plot-level random effects were included into this model through mixed-effects modeling. The results showed that the random effect of the stand density on the height-diameter relationship was substantially different at different classes of the site index, and the random effect of the site index was different for the different stand density classes. The nonlinear mixed-effects (NLME) height-diameter model coping with the interaction effects of the stand density and site index had a better performance than those of the NLME models with the random effect of the single variable of stand density or site index. To conclude, the inclusion of the interaction effects of stand density and site index could significantly improve the prediction accuracy of the height-diameter model for Larix olgensis Henry. The proposed model with the interactive random effects included can be applied for the accurate prediction of Larix olgensis tree height in northeast China. Full article

Land degradation, especially natural forest land degradation (NFLD), is a severe environmental concern in China. This natural disaster itself and its derivative control policies have caused some impacts on surrounding farmers’ livelihood level and strategies, but the literature on the sustainable livelihood of different households in NFLD vulnerability is limited, and there is an urgent need to bridge the gap and conduct studies on the sustainable livelihood of Changting, Libo, Lixian, Menghai and Wuxi, the typical NFLD-prone areas in China. A new livelihood sustainability index (LSI) including livelihood asset, livelihood strategy and sustainability engagement is constructed to assess the basic situation. The results showed that: (1) The overall LSI of five NFLD areas was not high, and the social, financial and natural assets, in particular, were relatively low. A disparity was found among the five areas, and the rank sequence of the LSI value was sorted in a descending order: Changting > Menghai > Libo > Lixian > Wuxi. (2) In detail, farmers in Changting had the relative highest LSI because of the inherent high value of livelihood assets, which constrain the scores of the livelihood strategy and sustainability engagement. (3) Households in Libo, Menghai and Lixian had middle level LSI scores. The relative low livelihood assets in Libo and Menghai drove parts of local farmers to carry out off-farm/forestry employment, leading to high scores of livelihood strategy, while farmers in Lixian had lower livelihood diversification scores and higher sustainability engagement due to their working content for the local forestry bureau. (4) The low scores of the livelihood asset and sustainability engagement restricted farmers in Wuxi. A discussion of LSI in the NFLD vulnerability was conducted to determine the characteristics and analyze the reasons. Accordingly, targeted policy recommendations were proposed to realize a sustainable livelihood in NFLD areas. Full article

Mung bean (Vigna radiata) plays an important role providing protein in the rice-based diet of the people in Bangladesh. In the coastal division of Barisal, our study area, the average farm size is less than 0.5 ha and individual fields measure about 0.10 ha. The availability of free Sentinel-2 optical satellite data acquired at a 10 m ground sampling distance (GSD) may offer an opportunity to generate crop area estimates in smallholder farming settings in South Asia. We combined different sources of in situ data, such as aerial photographs taken from a low flying manned aircraft, data collected on the ground, and data derived from satellite images to create a data set for a segment based classification of mung bean. User’s accuracy for mung bean was 0.98 and producer’s accuracy was 0.99. Hence, the accuracy metrics indicate that the random tree classifier was able to identify mung bean based on 10 m GSD data, despite the small size of individual fields. We estimated the mung bean area for 2019 at 109,416 ha, which is about 40% lower than the Department of Agricultural Extension estimates (183,480 ha), but more than four times higher than the 2019 data reported by the Bangladesh Bureau of Statistics (26,612 ha). Further analysis revealed that crop production tends to be clustered in the landscape by crop type. After merging adjacent segments by crop type, the following average cluster sizes resulted: 1.62 ha for mung bean, 0.74 ha for rice (Oryza sativa), 0.68 ha for weedy fallow and 0.40 ha for a category of other crops. This explains why 10 m GSD satellite data can be used for the identification of predominant crops grown in specific regions of South Asia. Full article

Floating offshore wind is being considered in northern California as indicated by the Bureau of Ocean Energy Management’s issuance of a lease consideration in the Humboldt Call Area. Humboldt County offers access to this enormous resource, but local electric load and transmission are limited. The potential impacts of offshore wind generators at three different scales were studied using a regional grid model of Humboldt County. Offshore wind generation was calculated using modeled wind speed data and 12-MW turbine specifications and integrated with projected load and historical generation. Offshore wind farms deployed in the Humboldt Call Area achieve annual capacity factors between 45% and 54% after losses and maintenance. Power output is variable between and within seasons, with full power output 30% of the time and no output approximately 20% of the time. Electricity from a 48-MW wind farm provides 22% of regional load with limited exports. A 144-MW wind farm serves 38% of local load, exporting 40% of its electricity with the extant 70-MW transmission capacity. A full build-out of 1836 MW would result in 88% curtailment with existing transmission. Across scenarios, offshore wind variability necessitates reliance on existing power plants to meet local demand in periods of low wind. Full article