Search Results - Random variables.

Understanding farmers’ technology adoption decisions: Input complementarity and heterogeneity by Abay, Kibrom A., Berhane, Guush, Taffesse, Alemayehu Seyoum, Koru, Bethlehem, Abay, Kibrewossen

“…The analysis also uncovers substantial unobserved heterogeneity effects, which induce heterogeneous impacts in the effect of the explanatory variables among farmers with similar observable characteristics. …”
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Experiential learning for groundwater governance in India study data by Meinzen-Dick, Ruth S., Falk, Thomas, Priyadarshini, Pratiti, Sanil, Richu, Duche, Vishwambhar, ElDidi, Hagar, Kosec, Katrina, Zhang, Wei, Melesse, Mequanint B., Carrillo, Lucia, Ferguson, Nathaniel, Shastri, Seema Vigneshwar, Reddy, Hemalatha, Foundation for Ecological Security Team

“…The impact evaluation follows a randomized control trial design, incorporating baseline and follow-up data from both treatment and control habitations. …”
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Population attributable fractions of farm vector tick (Rhipicephalus appendiculatus) presence on Theileria parva infection seroprevalence under endemic instability by Gachohi, John M., Kitala, P.M., Ngumi, P.N., Skilton, Robert A., Bett, Bernard K.

“…The primary objective of this study was to assess the impact of Rhipicephalus appendiculatus tick presence (exposure variable) on Theileria parva infection seroprevalence (outcome variable) in a group of cattle belonging to a farm using population attributable fractions (PAF). …”
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Household typology based analysis of livelihood strategies and poverty status in the Sudan Savannah of Nigeria: baseline conditions by Damisa, M.A., Sanni, S.A., Abdoulaye, Tahirou, Kamara, A.Y., Ayanwale, A.

“…This paper employs some baseline data of the Sudan Savanna Task Force in analysing household livelihood strategies and their poverty status. Stratified random sampling technique was employed in collecting data from the respondents. …”
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Diversity and Selection of Superior Algarrobos (Neltuma pallida) Phenotypes in the Natural Dry Forests of Peru for Sustainable Conservation and Genetic Improvement by Casas Niño, Sebastian, Baselly Villanueva, Juan Rodrigo, Salazar Hinostroza, Evelin Judith, Chumbimune Vivanco, Sheyla Yanet, Nauray , William, Tirabante Terrones, Nery, Ramírez Rojas, Max Augusto, Lozano Isla, Flavio

“…Dendrometric and phenotypic traits were recorded together with physiographic, climatic, and edaphic variables obtained from soil analyses and geographic information systems. …”
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Spatiotemporal analysis of wildfires and their relationship with climate and land use in the Gran Chaco and Pantanal ecoregions by Vidal Riveros, Cristina, Currey, Bryce, McWethy, David B, Ngo-Bieng, Marie Ange, Souza-Alonso, Pablo

“…Results of the fire pattern characterization were then used to model the probability of fire occurrence across each ecoregion (Random Forest, Generalized Linear Model, and Generalized Additive Model). …”
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Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands by Zwick, Mike, Cardoso, Juan Andres, Gutiérrez-Zapata, Diana María, Cerón-Muñoz, Mario, Gutiérrez, Jhon Freddy, Raab, Christoph, Jonsson, Nicholas, Escobar, Miller, Roberts, Kenny, Barrett, Brian

“…The best performing models varied by site and response variable, with Regularized Random Forest, Partial Least Squares, Random Forests, Bagged Multivariate Adaptive Regression and Bayesian Regularized Neural Networks being the top performing algorithms and Random Forest Stack being the best performing meta learner. …”
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Genetic differences in protein requirement of growing swine by Maner, JH, Rounsaville, TR, Gallo, JT, Pound, WG, VanVleck, LD

“…Of 66 pigs fed 16% protein (53 females, 13 males), 13 females and three males were chosen at random after 42 days on test to constitute the control breeding population for the next generation. …”
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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 by 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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Efecto de la disponibilidad de recursos sobre la eficiencia de uso y conservación de los nutrientes en función de las condiciones edafoclimáticas, edad y manejo de las plantaciones... by Martiarena, Rodolfo

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Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0 by Ali, Ashenafi, Erkossa, Teklu, Gudeta, Kiflu, Abera, Wuletawu, Mesfin, Ephrem, Mekete, Terefe, Haile, Mitiku, Haile, Wondwosen, Abegaz, Assefa, Tafesse, Demeke, Belay, Gebeyhu, Getahun, Mekonen, Beyene, Sheleme, Assen, Mohamed, Regassa, Alemayehu, Selassie, Yihenew G., Tadesse, Solomon, Abebe, Dawit, Walde, Yitbarek, Hussien, Nesru, Yirdaw, Abebe, Mera, Addisu, Admas, Tesema, Wakoya, Feyera, Legesse, Awgachew, Tessema, Nigat, Abebe, Ayele, Gebremariam, Simret, Aregaw, Yismaw, Abebaw, Bizuayehu, Bekele, Damtew, Zewdie, Eylachew, Schulz, Steffen, Tamene, Lulseged D., Elias, Eyasu

“…The data were cleaned and harmonized using the latest soil profile data template and prepared 14,681 profile data for modelling. Random Forest was used to develop a continuous quantitative digital map of 18 WRB reference soil groups at 250 m resolution by integrating environmental variables-covariates representing major Ethiopian soil-forming factors. …”
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Mapping peatland distribution and quantifying peatland below‐ground carbon stocks in Colombia's Eastern Lowlands by Uhde, A., Hoyt, A. M., Hess, L., Schmullius, C., Mendoza, E., Benavides, J. C., Trumbore, S., Martin-Lopez, Javier Mauricio, Skillings‐Neira, P. N., Winton, R. S.

“…Using new field data, high‐resolution Earth observation (EO), and a random forest approach, we mapped peatlands across Colombian territory East of the Andes below 400 m elevation. …”
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Effect of draught force and diet on dry-matter intake, milk production and live-weight change in non-pregnant and pregnant cows by Zerbini, E., Gebre-Wold, A., Demissie, D.

“…Eighteen F1 crossbred dairy cows (Friesian X Boran and Simmental X Boran) were allocated to one of three diet groups (H: natural pasture hay; H + 3: natural pasture hay + 3 kg concentrate; and H + 5: natural pasture hay + 5 kg concentrate) using a stratified random sampling procedure, with parity, milk production genotype, body weight and body condition score as blocking variables. …”
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Efficiency and its determinants among smallholder farming units supplying cassava to commercial starch processors in Nigeria: data envelopment analysis approach by Ojiako, I.A., Tarawali, G., Okechukwu, R.U., Chianu, J., Ezedinma, C.I., Edet, M.

“…However, a multi-stage random sampling technique was used to select a sample of 96 farming units from the clusters established under the project’s out-growers’ scheme. …”
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Climate change impact on cultivated and wild cacao in Peru and the search of climate change-tolerant genotypes by Ceccarelli, Viviana, Fremout, Tobias, Zavaleta, Diego, Lastra, Sphyros, Imán Correa, Sixto Alfredo, Arévalo Gardini, Enrique, Rodriguez, Carlos Armando, Cruz Hilacondo, Wilbert Eddy, Thomas, Evert

“…Methods: Drawing on 19,700 and 1,200 presence points of cultivated and wild cacao, respectively, we modelled their suitability distributions using multiple ensemble models constructed based on both random and target group selection of pseudo-absence points and different resolutions of spatial filtering. …”
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Una plataforma basada en machine learning para identificar variedades de duraznero con mayores chances de adaptación a escenarios climáticos futuros by Aballay, Maximiliano Martín, Chirino, Julian Santiago, Valentini, Gabriel Hugo, Sanchez, Gerardo

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Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods by García-Rodríguez, David, Catret, Pablo, Iglesias, Domingo J., Martínez, Juan J., López, Ernesto, García, Antonio

“…Conversely, the SVR demonstrated superior performance for the variables LE and CO2, with R2 values of 0.96 for both. …”
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Cambio climático al 2050 por efecto de la deforestación en la cuenca del río Monzón by Yack Luis, Rodas Burga

“…La relación entre la deforestación y las variables meteorológicas se evaluó mediante la correlación espacial de Pearson. …”
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Evaluación de tres métodos de propagación artifical de Hyacinthus orientalis L. en el valle 16 de Octubre, Chubut, Argentina = Evaluation of three propagation artificial methods o... by Lexow, Guillermo, Bobadilla, Segundo Evaristo

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Evaluación de sistemas agroforestales con café asociado con Eucalyptus deglupta ó Terminalia ivorensis e implicaciones metodológicas by Aguilar Carrillo, Amílcar

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