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1.
BMC Nutr ; 10(1): 98, 2024 Jul 11.
Article in English | MEDLINE | ID: mdl-38992741

ABSTRACT

BACKGROUND: In Rwanda, the prevalence of childhood stunting has slightly decreased over the past five years, from 38% in 2015 to about 33% in 2020. It is evident whether Rwanda's multi-sectorial approach to reducing child stunting is consistent with the available scientific knowledge. The study was to examine the benefits of national nutrition programs on stunting reduction under two years in Rwanda using machine learning classifiers. METHODS: Data from the Rwanda DHS 2015-2020, MEIS and LODA household survey were used. By evaluating the best method for predicting the stunting reduction status of children under two years old, the five machine learning algorithms were modelled: Support Vector Machine, Logistic Regression, K-Near Neighbor, Random Forest, and Decision Tree. The study estimated the hazard ratio for the Cox Proportional Hazard Model and drew the Kaplan-Meier curve to compare the survivor risk of being stunted between program beneficiaries and non-beneficiaries. Logistic regression was used to identify the nutrition programs related to stunting reduction. Precision, recall, F1 score, accuracy, and Area under the Curve (AUC) are the metrics that were used to evaluate each classifier's performance to find the best one. RESULTS: Based on the provided data, the study revealed that the early childhood development (ECD) program (p-value = 0.041), nutrition sensitive direct support (NSDS) program (p-value = 0.03), ubudehe category (p-value = 0.000), toilet facility (p-value = 0.000), antenatal care (ANC) 4 visits (p-value = 0.002), fortified blended food (FBF) program (p-value = 0.038) and vaccination (p-value = 0.04) were found to be significant predictors of stunting reduction among under two children in Rwanda. Additionally, beneficiaries of early childhood development (p < .0001), nutrition sensitive direct support (p = 0.0055), antenatal care (p = 0.0343), Fortified Blended Food (p = 0.0136) and vaccination (p = 0.0355) had a lower risk of stunting than non-beneficiaries. Finally, Random Forest performed better than other classifiers, with precision scores of 83.7%, recall scores of 90.7%, F1 scores of 87.1%, accuracy scores of 83.9%, and AUC scores of 82.4%. CONCLUSION: The early childhood development (ECD) program, receiving the nutrition sensitive direct support (NSDS) program, focusing on households with the lowest wealth quintile (ubudehe category), sanitation facilities, visiting health care providers four times, receiving fortified blended food (FBF), and receiving all necessary vaccines are what determine the stunting reduction under two among the 17 districts of Rwanda. Finally, when compared to other models, Random Forest was shown to be the best machine learning (ML) classifier. Random forest is the best classifier for predicting the stunting reduction status of children under two years old.

2.
Heliyon ; 9(9): e19041, 2023 Sep.
Article in English | MEDLINE | ID: mdl-37662738

ABSTRACT

Rainfed agriculture which is the mainstay of the Rwandan economy has been severely affected by prolonged droughts and climate change impacts, resulting in severe food insecurity. In the Eastern Province, the adoption of monocropping (MnC) systems at dissent driven by the CIP may critically worsen the rain-fed agricultural gains against mixed cropping (MxC) systems in drought conditions. Therefore, this study aimed to analyze and compare soil organic carbon (SOC) stocks and simulated maize biomass and grain yields under MnC and MxC systems in Kayonza District, Rwanda. Soil samples (n = 96) were collected in 0-30 and 30-60 cm depths following the stratified simple random sampling technique. The SOC stocks were determined following the guidelines of the FAO of 2018. The biomass and grain yield for the past 20 years (2001-2021) was simulated using a calibrated and validated AquaCrop model (version 6.1) using daily climate data obtained from RMA, and maize crop, raw soil, and land management features collected at the field. The data were analyzed using IBM SPSS software (version 25). The results show that the SOC stocks of MxC soils were significantly (p < 0.001) higher (67.4 ± 1.8 tC ha-1) than that of the MnC soils (52.0 ± 3.8 tC ha-1). The depths avowed more highly significant (p < 0.001) SOC stocks in topsoils (0-30 cm depth) than that of the subsoils (30-60 cm depth) in the two cropping systems. This indicates that MxC sequesters more carbon and revamps soil C pools than the MnC system. The results also indicate that the simulated biomass and grain yields were highly significantly (p < 0.001) higher more and stable in MxC than in MnC fields for the entire past 20 years. Harnessing these findings, as C pools were monitored and analyzed in this study, N-bio-chemistry dynamics should also be conducted thereby comparing its environmental pools and impacts to both below and above-ground ecotones.

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