Browsing by Author "Ramilan T"
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- ItemA Mixed-Methods Study of Factors Influencing Access to and Use of Micronutrient Powders in Rwanda(Johns Hopkins Center for Communication Programs, 2021-06-30) Dusingizimana T; Weber JL; Ramilan T; Iversen PO; Brough LThe World Health Organization recommends point-of-use fortification with multiple micronutrients powder (MNP) for foods consumed by children aged 6-23 months in populations where anemia prevalence among children under 2 years or under 5 years of age is 20% or higher. In Rwanda, anemia affects 37% of children under 5 years. The MNP program was implemented to address anemia, but research on factors affecting the implementation of the MNP program is limited. We conducted a mixed-methods study to examine the factors influencing access to and use of MNP among mothers (N=379) in Rutsiro district, northwest Rwanda. Inductive content analysis was used for qualitative data. Logistic regression analysis was used to determine factors associated with the use of MNP. Qualitative results indicated that the unavailability of MNP supplies and distribution issues were major barriers to accessing MNP. Factors influencing the use of MNP included mothers' perceptions of side effects and health benefits of MNP, as well as inappropriate complementary feeding practices. Mothers of older children (aged 12-23 months) were more likely to use MNP than those of younger children (aged 6-11 months) (adjusted odds ratio [aOR]=3.63, P<.001). Mothers whose children participated in the supplementary food program were nearly 3 times more likely to use MNP than those whose children had never participated in the program (aOR=2.84, P=.001). Increasing household hunger score was significantly associated with lower odds of using MNP (aOR=0.80, P=.038). Mechanisms to monitor MNP supply and program implementation need to be strengthened to ensure mothers have access to the product. MNP program implementers should address gaps in complementary feeding practices and ensure mothers have access to adequate complementary foods. L'Organisation Mondiale de la Santé recommande l'enrichissement de l'alimentation à domicile (enrichissement sur le point d'utilisation) à l'aide des poudres de micronutriments multiples (PMN) pour les aliments consommés par les enfants âgés de 6 à 23 mois dans les populations où la prévalence de l'anémie chez les enfants de moins de 2 ans ou 5 ans est de 20% ou plus. Au Rwanda, l'anémie touche 37% des enfants de moins de 5 ans et le programme de PMN a été mis en œuvre pour lutter contre l'anémie. Cependant, la recherche sur les facteurs qui affectent la mise en œuvre du programme de PMN est limitée. Nous avons mené une étude par méthodes mixtes pour examiner les facteurs qui influencent l'accès des mères (n=379) à la PMN et son utilisation dans le district de Rutsiro, au nord-ouest du Rwanda. L'analyse du contenu inductif a été utilisée pour les données qualitatives. Pour déterminer les facteurs associés à l'utilisation des PMN, une régression logistique a été utilisée. Les résultats qualitatifs ont indiqué que l'indisponibilité des approvisionnements en PMN et les problèmes de distribution constituaient des obstacles majeurs à l'accès à la PMN. Les facteurs qui influencent l'utilisation des PMN comprenaient les perceptions, chez les mères, des effets secondaires et des avantages des PMN pour la santé, ainsi que des pratiques d'alimentation complémentaire inappropriées. Les mères d'enfants plus âgés (12 à 23 mois) étaient plus susceptibles d'utiliser la PMN que celles d'enfants plus jeunes (6 à 11 mois) (odds ratio ajusté [ORA]=3,63, P<0,001). Les mères des enfants qui avaient participé au programme d'alimentation complémentaire étaient près de 3 fois plus susceptibles d'utiliser la PMN que celles des enfants qui n'avaient jamais participé au programme (ORA=2,84, P=0,001). L'augmentation du score de faim dans les ménages était significativement associée à des chances plus faibles d'utiliser la PMN (ORA=0,80, P=0,038). Les mécanismes de suivi de l'approvisionnement en PMN et de la mise en œuvre du programme doivent être renforcés pour s'assurer que les mères ont accès au produit. Les responsables de la mise en œuvre du programme de PMN devraient combler les lacunes au niveau des pratiques d'alimentation complémentaire et veiller à ce que les mères aient accès à des aliments complémentaires adéquats.
- ItemAssessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models(MDPI AG, 2023-03-08) Lyu H; Grafton MC; Ramilan T; Irwin M; Sandoval - Cruz E; Díaz-Varela, RA
- ItemNitrogen decisions for cereal crops: a risky and personal businessFarquharson R; Chen D; Yong L; Liu D; Ramilan TCereal crops principally require Nitrogen (N) and water for growth. Fertiliser economics are important because of the cost at sowing with expectation of a financial return after harvest. The production economics framework can be used to develop information for ‘best’ fertiliser decisions. But the variability of yield responses for rainfed production systems means that fertiliser decisions are a risky business. How do farmers make such decisions, and can economics give any guidance? Simulated wheat yield responses to N fertiliser applications show tremendous variation between years or seasons. There are strong agronomic arguments for a Mitscherlich equation to represent yield responses. Plots of the 10th, 50th and 90th percentiles of yield response distributions show likely outcomes in ‘Poor’, ‘Medium’ and ‘Good’ seasons at four Australian locations. By adding the prices for Urea and wheat we predict that the ‘best’ decisions vary with location, soil, and (sometimes) season. We compare these predictions with typical grower fertiliser decisions. Australian wheat growers understand the yield responses in their own paddocks and the relative prices, so they are making relevant short-term fertiliser decisions. These are subjective or personal decisions. Myanmar smallholders grow rice and maize in the Central Dry Zone, with relatively low levels of fertiliser and low crop yields. They have pre-existing poverty, high borrowing costs and are averse to risky outcomes. A Marginal Rate of Return (MRR) analysis with a hurdle rate of 100% is illustrated for the Australian locations, and this approach will be tested in Myanmar.
- ItemUsing Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality(MDPI AG, 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu DThe traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 ◦Brix, and coefficient of determination (R2 ) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 ◦Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 ◦Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.