Resultados de búsqueda - Random variables.

Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling por Akpoti, K., Kabo-bah, A.T., Dossou-Yovo, Elliott Ronald, Groen, T.A., Zwart, Sander J.

“…In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). We used a set of predictors that were grouped under climatic variables, agricultural water productivity and soil water content, soil chemical properties, soil physical properties, vegetation cover, and socio-economic variables. …”
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Assessing complementary synergies for integrated crop–livestock systems under conservation agriculture in Tunisian dryland farming systems por Dhehibi, Boubaker, Fouzai, Ayoub, Frija, Aymen, Abdeladhim, Mohamed Arbi, M'hamed, Hatem Cheikh, Ouerghemmi, Hassen, Rekik, Mourad

“…The second objective is to explore complementarities, synergies, and economies of diversification across the different production system components of these crop-livestock producers using the cross-partial derivative framework of output variables in the distance function. A simple random sampling process was employed to select and survey 59 CL smallholders operating under conservation agriculture. …”
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Comparative study on the performance of dairy cooperative input and output marketing in Astbie Womerta, Alamata and Enderta woreda in Tigray region, Ethiopia por Mesfin, A.

“…All cooperatives except one were included in the study and a random sample of 120 respondents was selected based on probability proportionate to size. …”
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Modelling effective soil depth at field scale from soil sensors and geomorphometric indices por Castro Franco, Mauricio, Domenech, Marisa Beatriz, Costa, Jose Luis, Aparicio, Virginia Carolina

“…The aim of this study was to develop a statistic model to predict spatial patterns of ESD using apparent electrical conductivity at two depths: 0-30 (ECa_30) and 0-90 (ECa_90) and geomorphometric indices. To do this, a Random Forest (RF) analysis was applied. RF was able to select those variables according to their predictive potential for ESD. …”
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The Phenology of Coffea arabica var. Esperanza L4A5 Under Different Agroforestry Associations and Fertilization Conditions in the Caribbean Region of Costa Rica por Morales Peña, Victor Hugo, Mora Garcés, Argenis, Virginio Filho, Elias de Melo, Villatoro Sánchez, Mario, Pazmiño Pachay, Willy William, Chanto Ares, Esteban

“…To analyze the relationships between environmental factors, tree cover, fertilization, and the phenological stages, we employed multiple linear regression (MLR), which revealed that both tree cover and physical and chemical fertilizations had significant effects on the presence of developed floral nodes and, consequently, on fruit production. Furthermore, the random forest (RF) model was applied to capture complex interactions between variables and to rank the importance of meteorological factors, tree cover, and fertilization practices. …”
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A mixed-methods approach to understanding knowledge of mosquito-borne infections and barriers for protection in Hanoi, Vietnam por Chapot, L., Thang Nguyen-Tien, Long Pham-Thanh, Hung Nguyen-Viet, Craven, L., Lindahl, Johanna F.

“…A total of 117 participants recruited using a stratified random sampling method were interviewed in three districts of Hanoi. …”
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Impact of youth-in-agribusiness program on employment creation in Nigeria por Bello, L.O., Baiyegunhi, L.J., Mignouna, Djana B., Adeoti, R., Dontsop Nguezet, Paul M., Abdoulaye, Tahirou, Manyong, Victor, Bamba, Z., Awotide, B.A.

“…Therefore, this study examined the impact of the YIA program on creating gainful employment among the youth. Multistage random sampling was used to obtain cross-sectional data from 668 youth in Southwestern Nigeria. …”
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Annual forest cover conditions across the Southwestern Amazon, 2003-2021 por Reygadas, Yunuen

“…The algorithm classifies forest covers by training a random forest model with sixty-six metrics derived from six time series variables (i.e., the Normalized Difference Vegetation Index, two shortwave infrared bands, two Normalized Difference Water Indices, and the Soil-Adjusted Vegetation Index) from which eleven descriptive statistics are calculated. …”
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The epidemiology of good nutritional status among children from a population with a high prevalence of malnutrition por Pryer, Jane A., Rogers, S., Rahman, Atiur

“…To identify socio-economic demographic and environmental factors that predict better height-for-age for children under 5 years of age in a Dhaka slum population.A panel survey, conducted between 1995 and 1997. A random sample of households was selected. Socio-economic, demographic and environmental variables were collected monthly by questionnaire and nutritional status was assessed.Dhaka slums in Bangladesh.Three hundred and ninety-two children, surveyed in September–November 1996.Height-for-ageZ-score (HAZ) above −2.Thirty-one per cent of children had HAZ <−2. …”
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Enhancing groundwater predictions by incorporating response lag effects in machine learning models por Yang, F., Zhang, X., Yang, J., Zhang, J., Dai, Q., Zhu, S.

“…We performed a lag analysis utilizing long-term data to ascertain correlations between groundwater levels and climatic variables. Two groundwater prediction models using the random forest algorithm were developed, one incorporating the lag effect and the other excluding it. …”
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Papua New Guinea Rural Household Survey, 2023 por International Food Policy Research Institute

“…It is important to note that the survey is not nationally representative; however, given the careful random selection of survey areas, we expect that generalizable relationships between variables affecting socio-economic and other development outcomes in rural PNG communities will be consistently observed across representative samples and in this survey. …”
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Sensitivity and uncertainty propagation in coupled models for assessing smallholder farmer food security in the Olifants River Basins, South Africa por Magombeyi, Manuel Simba, Taigbenu, A.E.

“…Scatter plots inferred the most sensitive variables affecting family balance, while the Monte Carlo method, using random sampling, was used to propagate the uncertainty in the model inputs to produce family balance probability distributions. …”
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Ecological adaptation of the shea butter tree (Vitellaria paradoxa CF. Gaertn.) along climatic gradient in Bénin, West Africa por Kakai RG, Akpona TJD, Assogbadjo, A.E., Gaoue OG, Chakeredza, Sebastian, Gnangle PC, Mensah, G.A., Sinsin B

“…Principal component analysis was performed on the dendrometric variables, and the first three components were correlated with the climatic parameters. …”
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Genetic diversity in South American Colletotrichum gloeosporioides isolates from Stylosanthes guianensis, a tropical forage legume por Kelemu, Segenet, Skinner, DZ, Badel, JL, Moreno, CX, Rodríguez, M.X., Fernandes, C.D., Charchar, Maria José, Chakraborty, S.

“…The degree of genetic diversity of 127 Colletotrichum gloeosporioides isolates from Stylosanthes guianensis genotypes in South America was measured at the molecular level by random amplified polymorphic DNA (RAPD) with nine arbitrary primers of 10 bases, and by restriction fragment length polymorphism (RFLP) with a non-LTR (long terminal repeats) retrotransposon DNA sequence. …”
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Foraging is determinant to improve smallholders’ food security in rural areas in Mali, West Africa por N’Danikou, S., Vodouhe, R.S., Bellon, M.R., Sidibé, A., Coulibaly, H.

“…Elements of the five livelihood assets were assessed through questionnaire surveys with a random sample of 180 households, and six focus group discussions in three communities along the rural-urban continuum, in Southern Mali. …”
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Positive association between Brucella spp. seroprevalences in livestock and humans from a cross-sectional study in Garissa and Tana River Counties, Kenya por Kairu-Wanyoike, S., Nyamwaya, D., Wainaina, M., Lindahl, Johanna F., Ontiri, E., Bukachi, S., Njeru, I., Karanja, J., Sang, R., Grace, Delia, Bett, Bernard K.

“…In livestock, older animals and those sampled in Hola had significantly higher seroprevalences than younger ones or those sampled in Bura. Herd and village random effects were significant and ICC estimates associated with these variables were 0.40 (95% CI: 0.22–0.60) and 0.24 (95% CI: 0.08–0.52), respectively. …”
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Agricultura guiada por datos para maíz en Chiapas, México y altiplano occidental de Guatemala por Navarro Racines, Carlos, Jaimes, Diana, Llanos, Lizeth, Estrada, Oscar, Barrios, Camilo, Agudelo, Diego, Jimenez, Daniel, Gardeazabal, Andrea, Dorado, Hugo, Ramirez Villegas, Julian

“…A partir de más de 4,500 registros productivos en Chiapas, México, y 6,500 observaciones en parcelas de pequeños productores del altiplano occidental de Guatemala, se aplicaron modelos Random Forest para identificar factores determinantes del rendimiento y sus interacciones. …”
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Cultural and contextual drivers of triple burden of malnutrition among children in India por Singh, Shri Kant, Chauhan, Alka, Sharma, Santosh Kumar, Puri, Parul, Pedgaonkar, Sarang, Dwivedi, Laxmi Kant, Taillie, Lindsey Smith

“…Multilevel analysis reveals that child, maternal, and household variables play a substantial role in determining malnutrition burden in India. …”
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Agricultural mechanization and gendered labor activities across sectors: Micro-evidence from multi-country farm household data por Takeshima, Hiroyuki, Diao, Xinshen

“…This study aims to partly fill these knowledge gaps through micro-evidence from 8 countries (Ethiopia, Ghana, Nigeria, Tanzania, India, Nepal, Tajikistan and Vietnam), using several nationally representative panel data and supplementary data, and applying Correlated-Random-Effects Double-Hurdle models with Instrumental-Variables. …”
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Utilizing Artificial Intelligence and Remote Sensing to Detect Prosopis juliflora Invasion: Environmental Drivers and Community Insights in Rangelands of Kenya por Paliwal, Ambica, Mhelezi, Magdalena, Galgallo, Diba, Banerjee, Rupsha R., Malicha, Wario, Whitbread, Anthony M.

“…Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. …”
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