Search Results - Random variables.

Estimación de la eficiencia de métodos territoriales de muestreo de la roya del café (Hemileia vastatrix) en Honduras, El Salvador, Panamá y República Dominicana by Treminio Polanco, Edwin A.

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Identifiering av fel i skogliga beståndsregister med hjälp av satellitdata by Eriksson, Jonatan

“…To evaluate Metrias method, a number of random chosen stands marked as deviant where measured in field. …”

Between all-for-one and each-for-himself: On-farm competition for labour as determinant of wetland cropping in two Beninese villages by Paresys, L., Malézieux, E., Huat, J., Kropff, K.J., Rossing, W.A.H.

“…Farm typologies were developed based on random samples of 51 out of 134 farms (38%) from Zonmon and 50 out of 146 farms (34%) from Pelebina by combining principal component analysis and Ward's minimum variance clustering. …”
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Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches by Cai, Y., Guan, K., Lobell, D. B., Potgieter, A.B., Wang, S., You, Liangzhi

“…We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. …”
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Geospatial characterization of climate-smart agroforestry in two contrasting physiographic zones of Rwanda by Ntawuruhunga, D., Ngowi, E.E., Mangi, H.O., Salanga, R.J., Shikuku, Kelvin Mashisia

“…Cross-validation using ground-truthing information (field visit and collection of GPS-based ground coordinates of random locations of actual CSAF) mostly supported the generated CSAF suitability maps (nearly 91 % of ground-based locations supported the model output). …”
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Parametric and machine learning approaches to examine yield differences between control and treatment considering outliers and statistical biases: The case of insect resistant/herb... by Falck-Zepeda, José B., Zambrano, Patricia, Sanders, Arie, Trabanino, Carlos Rogelio

“…Second, we discuss and apply four types of approaches that can be used to obtain robust performance estimates for yield and cost differentials including: 1) Robust Instrumental Variables, 2) Instrumental Variable Regressions, and 3) Control/Treatment, and 4) Machine Learning methods that are amenable to robust strategies to deal with outliers including Random Forest and a Stacking regression approach that allows for a number of “base learners” in order to examine the pooled 2008 and 2012 Honduras field surveys. …”
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Surface water area dynamics of the major lakes of Ethiopia (1985–2023): A spatio-temporal analysis by Getaneh, Yonas, Abera, Wuletawu, Abegaz, Assefa, Tamene, Lulseged D.

“…We extracted the time-series water surface area of the lakes from Landsat images in the Google Earth Engine (GEE), using the Modified Normalized Water Index (MNDWI) and machine learning (Random Forest) methods. Random Forest (RF) outperformed MNDWI across many lakes, especially when water body spectral characteristics became complex. …”
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Associations between exposure to nutrition, WASH interventions and children’s academic performance in Ethiopia: A systematic review and meta-analysis by Adugna, Yimer Mihretie, Ayelign, Abebe, Zerfu, Taddese Alemu

“…The meta-analysis used a random-effects model in Stata and reported pooled RRs with 95% CIs. …”
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Ekologisk kompensation: en utvärdering av åtgärder för gölgroda (Pelophylax lessonae) i Forsmark by Karlsson, Malin

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Predicción de la variabilidad de la productividad de pasturas para la delimitación de zonas de manejo en suelos ganaderos by Cicore, Pablo Leandro

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Estudio comparativo de la calidad interna del huevo en tres genotipos de ponedoras camperas by Romera, Bernardo Martín, Savoy, Juan Pablo, Perrotta, Cristian Hernán, Antruejo, Alejandra Edith, Canet, Zulma Edith, Dottavio, Ana María, Di Masso, Ricardo José

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Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) p... by Brewer, K., Clulow, A.D., Sibanda, M., Gokool, S., Odindi, J., Mutanga, O., Naiken, V., Chimonyo, Vimbayi Grace Petrova, Mabhaudhi, Tafadzwanashe

“…Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. …”
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Host discrimination in the fruit fly parasitoid Diachasmimorpha longicaudata : evidence from virgin female behaviour and egg distribution patterns by Devescovi, Francisco, Bachmann, Guillermo Enrique, Nussenbaum, Ana Laura, Viscarret, Mariana Mabel, Cladera, Jorge Luis, Segura, Diego Fernando

“…Indirect methods showed females with different behaviours for both host species: complete discrimination, non-random (with superparasitism), and random distributions. …”
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Social network effects on consumer willingness to pay for biofortified crops by Muange, Elijah N., Oparinde, Adewale

“…The study used the Becker-DeGroot-Marshak mechanism to elicit consumer WTP in the absence and presence of radio messages providing positive and negative frames of information on nutritional benefits of HIB varieties at different frequencies. Instrumental variable and random effects models were used to assess the determinants of WTP.…”
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Análisis y evaluación de estrategias tecnológicas y productivas de los sistemas de producción ganaderos predominantes en el centro-sur de Corrientes by Calvi, Mariana

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Habitat modeling for rustic bunting (Emberiza rustica) territories in boreal Sweden by Larsson, Emil

“…Swampy forests in boreal Sweden were surveyed using playback and habitat variables were measured at both presence points and randomly generated points. …”
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Beyond pastures, look at plastic: Using Sentinel-2 imagery to map silage bags to improve understanding of cattle intensity by Fernandez, Pedro David, Gärtner, Phillipp, Nasca, Jose Andres, Rojas, Tobias, Gasparri, Nestor Ignacio

“…With freely available Sentinel-2 satellite imagery, we mapped for the first time polyethylene silage bags used for forage conservation in a year with the Random Forest algorithm, and proposed them as a spatial indicator of cattle intensity. …”
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Towards site and context-specific fertilizer recommendations for maize production in Northern Ghana using AgWise framework (Machine learning component) by Jizorkuwie, Abdul-latif Baamonyor, Tilaye, Asmalu, Amankwa-Yeboah, Patricia, Assefa, Feben, Masoud, Jalaludeen, Akpatsu, Isaac Boatey, Abera, Wuletawu

“…The random forest model emerged as the top performer through rigorous evaluation, based on MAE, RMSE, and R², which was utilized in the prediction. …”
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Soil Organic Carbon Stock Estimates with Uncertainty across Latin America by Guevara, Mario, Olmedo, Guillermo Federico, Stell, Emma, Yigini, Yusuf, Hernández Arelano, Carlos, Arevalo, Gloria, Arroyo-Cruz, Carlos Eduardo, Bolivar, Adriana, Bunning, Sally, Bustamante Canas, Nelson, Cruz-Gaistardo, Carlos Omar, Davila, Fabian, Dell Acqua, Martín, Encina, Arnulfa, Fontes, Fernanda, Hernández Herrera, José A., Pereira, Gonzalo, Schulz, Guillermo, Spence, Adrian, Vazques, Gustavo

“…Four companion files include: a 133-band GeoTiff containing the environmental predictor variables for SOC across Latin America, a .csv file with descriptions of the environmental variables, a shapefile (.shp) of the point soil characterization data with SOC stock estimates and a *.kmz file to display the same.…”
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Efecto del momento de corte y de productos secantes sobre la calidad de pasas de uva de la variedad Flame seedless by Morales, Maria Leticia

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