Search Results - Random variables.

Marriage between variable selection and prediction methods to model plant disease risk by Suarez, Franco, Bruno, Cecilia, Kurina Giannini, Franca, Gimenez, Maria, Rodriguez Pardina, Patricia, Balzarini, Mónica Graciela

“…The disease risk predictors were constructed with a logistic linear regression model (LR) and the random forest (RF) algorithm, using all the available variables and the subgroups of variables selected by each feature selection method. …”
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Evaluation of variability in Striga aspera, Striga hermonthica and their hybrids using morphological characters and random amplified polymorphic DNA markers by Aigbokhan, E.I., Berner, D.K., Musselman, L.J., Mignouna, Hodeba D.

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Analyzing inflow to Faye reservoir sensitivity to climate change using CMIP6 and random forest algorithm by Akaffou, F. H., Obahoundje, Salomon, Didi, S. R. M., Koffi, B., Coulibaly, W. B., Habel, M., Kadjo, M. M. F., Kouassi, K. L., Diedhiou, A.

“…In the era of Climate Change and Climate Variability (CC and CV), renewable energy sources such as Hydropower (HP) have a significant role to play in mitigation. …”
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Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy by Silva, ‪João Vasco, Heerwaarden, Joost van, Reidsma, Pytrik, Laborte, Alice G., Tesfaye, Kindie, Ittersum, Martin K. van

“…Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. …”
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Seasonality and day-to-day variability of dietary diversity: Longitudinal study of pregnant women enrolled in a randomized controlled efficacy trial in rural Burkina Faso by Hanley-Cook, Giles T., Argaw, Alemayehu, de Kok, Brenda, Toe, Laeticia Celine, Dailey-Chwalibóg, Trenton, Huybregts, Lieven

“…The natural day-to-day variability in food group consumption during gestation is unknown. …”
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Estimation and use of a multivariate parametric model for simulating heteroscedastic, correlated, non-normal random variables: the case of corn-belt corn, soybeans and wheat yields by Ramírez, O.A.

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Integrating remote sensing and in-situ data to determine climate diversity and variability in cocoa systems in the provinces of Jaén and San Ignacio, Cajamarca (NW Perú) by Atalaya Marin, Nilton, Goña Goñas, Malluri, Tineo Flores, Daniel, Chuquibala Checan, Beimer, Arce Inga, Marielita, Tarrillo Julca, Ever, Alvarez Robledo, Yeltsin Abel, Tafur Culqui, Josué, Cabrera Hoyos, Héctor Antonio, Gómez Fernández, Darwin

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Variability in Fruit Production of Carapa Guianensis Associated with Edaphoclimatic Factors in the Amazon by Angulo Villacorta, Carlos Darwin, Silva da Conceição, Denilson, Chuchon Remon, Rodolfo Juan, Manigat, Donald, Antunez Jimenez, Lorena, de Toledo, José Julio

“…Environmental variables were evaluated using Random Forest models. …”
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Classification of ground lichen using Sentinel-2 and airborne laser data by Larsson, Helene

“…Classifcation of lichen coverage was carried out with the Random Forest algorithm, and 90 field plots were used as training data. …”

Genetic diversity in cowpea as revealed by random amplified polymorphic DNA by Mignouna, Hodeba D., Ng, N.Q., Ikea, J., Thottappilly, G.

“…In this study, random amplified polymorphic DNA (RAPD) markers were used to assess genetic diversity in cowpea (Vigna unguiculata (L). …”
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Modeling of Effective Leaf Area Index by Selin, Lina

Comparative evaluation of the contributions of soil physicochemical properties to variations in the yields of four major staple food crops in eastern Nigeria by Asadu, C., Dixon, A., Okechukwu, R.

“…The data were from three replicates of two randomized complete block design experiments sited in a newly cleared forest and on previously cultivated land both in Nsukka, eastern Nigeria. …”
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Short range spatial variability of soil physico-chemical variables related to earthworm clustering in a Neotropical gallery forest by Jiménez, J.J., Decaëns, Thibaud, Amézquita Collazos, Edgar, Rao, Idupulapati M., Thomas, Richard J., Lavelle, Patrick M.

“…Partial Mantel test revealed specific significant relationships between soil variables and some species. The earthworm community of the GF displayed a random structure in a spatially clumped soil environment, and our results suggested that spatial distribution observed for some species could be the result of preferential selection of soil environmental factors. …”
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From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach by Carbajal, Mariella, Ramirez, David A., Turin Canchaya, Cecilia Claudia, Schaeffer, Sean M., Konkel, Julie, Ninanya, Johan, Rinza, Javier, De Mendiburu, Felipe, Zorogastua, Percy, Villaorduña, Liliana, Quiroz, Roberto

“…A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. …”
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Climate variability and change: perceptions, experiences and realities by Rao, K.P.C., Oyoo A

“…While the role and significance of some of these factors on productivity and profitability can be perceived more easily due to their relative predictability, extreme variability in climate and the random nature of that variability makes it difficult for farmers to accurately perceive trends in climate. …”
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A model of labeling with horizontal differentiation and cost variability by Saak, Alexander E.

“…We study optimal disclosure of variety by a multiproduct firm with random costs. The prices for labeled varieties are increasing functions of the cost differential and do not reveal which variety is cheaper to produce. …”
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A model of labeling with horizontal differentiation and cost variability by Saak, Alexander E.

“…We study optimal disclosure of variety by a multi-product firm with random costs. In our model there are two varieties that are horizontally differentiated and differ in overall quality, but buyers cannot distinguish between them without labels. …”
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Genetic variability and evolutionary dynamics of viruses of the family Closteroviridae. by Rubio, Luis, Guerri, José, Moreno, Pedro

“…Here we performed an updated analysis of sequences available in Genbank and reviewed present knowledge on the genetic variability and evolutionary processes of viruses of the family Closteroviridae. …”
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Spatial and Temporal Variability of Transplanted Rice At the Field Scale by Dobermann, Achim, Pampolino, Mirasol F., Neue, Heinz-Ulrich

“…At all growth stages, two samples of three randomly selected hills were sufficient to measure plant height in a field. …”
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Pasture and grassland productivity and foliar elongation of wheatgrass in Southern Patagonia = Productividad de pastura y pastizal y elongación foliar de agropiros en la Patagonia... by Utrilla, Victor Ricardo, Andrade, Miguel Alberto, Vargas, Paola Patricia, Alsina, María Luz, Aguilar, Romina, Galván, Julieta

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