Search Results - Random variables.

Genetic and phenotypic parameters and trends of reproduction traits in Kenya Boran cows by Okeyo Mwai, Ally, Mosi, R.O., Rege, J.E.O.

“…The mixed models included the same fixed effects plus animal and permanent environment as random effects. Variance components and genetic parameter estimates were obtained from univariate analyses within ranch, using the Derivative Free Restricted Maximum Likelihood (DFREML) program of Meyer (1991). …”
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Evaluación ecofisiológica de trece leguminosas nativas con potencial forrajero para la producción animal en el Espinal, Tolima by Barragán Quijano, Eduardo, Vanegas R., Miguel Alfonso

“…The evaluation was based on climatic variables such as rainfall, temperature at 5 cm below soil and environmental temperature; the morpho-physiological variables leaf area and leaf dry weight were evaluated by using the semidesfrucfive analysis technique for analogy; and experimental design of complete randomized blocks with sampling was used. …”
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Contenidos de nitrógeno y fósforo del suelo ante un cambio de cobertura y condición topográfica by Besteiro, Sebastián Ignacio, Descalzo, A.I.B.

“…Posteriormente, se incorporó la variable de sitio como factor random en un análisis de modelos mixtos. …”
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Effects of bacteriocin-producing Lactiplantibacillus plantarum on bacterial community and fermentation profile of whole-plant corn silage and its in vitro ruminal fermentation, mic... by Ziqian Li, Usman, S., Jiayao Zhang, Yixin Zhang, Su, R., Hu Chen, Qiang Li, Mengya Jia, Amole, Tunde A., Xusheng Guo

“…In addition, treatment with bacteriocin-producing strains increased the in vitro DM digestibility (P < 0.05) and decreased the CH4 production (P < 0.05). The results of random forest and clustering analyses at the genus level showed that ATCC14917 increased the relative abundance of the influential variable Bacillus compared to that in the control group, whereas CICC24194 decreased the relative abundance of the influential variable Ruminococcaceae UCG-005. …”
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Suboptimal nutritional status of school-age children in Addis Ababa: evidence from the analysis of socioeconomic, environmental, and behavioral factors by Adugna, Yimer Mihretie, Ayelign, Abebe, Zerfu, Taddese Alemu

“…A total of 309 study participants were randomly selected from 10 schools. Data were entered into Epidata version 3.1 and exported to SPSS version 23.0 for analysis. …”
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Assessing farmers` knowledge of weed species, crop type and soil management practices in relation to soil quality status in mai-negus catchment, northern Ethiopia by Tesfahunegn, Gebreyesus Brhane, Tamene, Lulseged D., Vlek, Paul L.G., Mekonnen, Kindu

“…Fifty‐two farmer household heads were chosen randomly for questionnaire interview. The results showed significant (p ≤ 0·05) differences in the proportion of respondents who used different crop‐and‐soil management practices. …”
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Prevalence and risk factors for gastrointestinal parasites in small-scale pig enterprises in Central and Eastern Uganda by Roesel, Kristina, Dohoo, I., Baumann, M., Dione, Michel M., Grace, Delia, Clausen, Peter-Henning

“…All animals tested negative for Fasciola spp. and Balantidium coli. Thirty-five variables were included in univariable analyses with helminth infection as the outcome of interest. …”
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Evaluación de la calidad nutricional en pasturas de gramíneas perennes relacionada con el estado fenológico y el ambiente en el Sur de Santa Cruz by Gil, Manuel Jesús, Utrilla, Victor Ricardo

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Impact of climate change on the productivity and adaptation of Ethiopia's Bonga and Menz sheep breeds by Tesema, Zeleke, Getachew, Tesfaye, Belay, Berhanu, Amha, Yosef, Rekik, Mourad, Rischkowsky, Barbara A., Besufkad, Shanbel, Abate, Zelalem, Bekele, Tamrat, Demissie, Teferi, Solomon, Dawit, Haile, Aynalem

“…This study used the 14 years (2009–2022) of productive, reproductive, pedigree, and climate data to derive resilience and stability phenotypes using a random regression model fitting to the reaction norm function and genetic parameter estimates were estimated from a linear mixed model. …”
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Prevalence and risk factors for lameness in dairy cattle on selected farms located in Dessie and Kombolcha, Northeast Ethiopia by Mekonin, H.A., Reda, A.A., Assen, Alula A., Assen, A.M.

“…The herd-level prevalence was calculated as the total number of positive herds divided by the total number of herds sampled. After variable screening using univariable analysis, separate multivariable mixed-effects logistic regression models that included farm as a random effect were fitted to identify risk factors for lameness at both the animal and herd levels. …”
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Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models by Hussain, S., Arshad, M., Cheema, Muhammad Jehanzeb Masud, Qamar, M. U., Wajid, S. A., Daccache, A.

“…A machine learning (ML) model, Random Forest (RF), was employed to accurately predict soil moisture content at 15 cm depth. …”
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Agronomic levers to increase maize and soybean productivity across the Chinyanja Triangle, Southern Africa by Chiduwa, Mazvita, Nyagumbo, Isaiah, Omondi, John Okoth, Mkuhlani, Siyabusa, Masikati, Patricia, Malunga, Innocent, Mwila, Mulundu, Simwaka, Pacsu, Mapaure, Mateus, Banda, Andson, Hougni, Deo-Gratias Judrita Mawugnon, Ndour, Adama, Fantaye, Kindie Tesfaye, Snapp, Sieglinde, Silva, João Vasco

“…A machine learning approach combining random forest and Shapley values was used to explain yield variability and identify yield constraints. …”
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Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests by Besnard, H., Carvalhais, N., Arain, M.A., Black, A., Brede, B., Buchmann, Nina, Chen, J., Clevers, J.G.P.W., Dutrieux, L.P., Gans, F., Herold, M., Jung, M., Kosugi, Y., Knohl, A., Law, B.E., Paul-Limoges, E., Lohila, A., Merbold, Lutz, Roupsard, O., Valentini, R., Wolf, S., Zhang, X., Reichstein, Markus

“…This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. …”
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Effects of tree retention on cavity-nesting birds in northern Sweden by Domingo Gómez, Eva

“…It was conducted on a random selection of 100 clear-cuts up to 5 years old within a 20 km radius in the vicinity of Umeå, northern Sweden. …”
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Agronomic behavior of cowpea varieties in non-alluvial soils of the Peruvian Amazon by Angulo Villacorta, Carlos Darwin, Mathios Flores, Marco Antonio, Sangama Arirama, Misael Nemecio, Racchumi García, Alfredo

“…The experiment was performed in completely randomized design with four treatments and four repetitions. …”
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Optimizing water and nitrogen application for neglected horticultural species in tropical sub-humid climate areas: a case of African eggplant (Solanum aethiopicum L.) by Mwinuka, P.R., Mbilinyi, B.P., Mbungu, W.B., Mourice, S.K., Mahoo, H.F., Schmitter, Petra S.

“…To reduce the yield gap, a randomized split-plot design set up with irrigation as a main and nitrogen (N) treatments as a sub-factor. …”
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Rendimiento de pasto llorón, Eragrostis curvula (Nees) cv. Ermelo, en función de algunas propiedades edáficas by Merino, C.A, Rosell, R.A, Gargano, A.O.

“…To predict crop responses it is recommended that only the first 4 variables in the equation which includes all soils and the first 3 variables in the equation excluding soil number 16 be considered.…”
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Assessment of coffee quality and its related problems in Jimma Zone of Oromia Regional State by Abasanbi, A.A.

“…A total of 14 explanatory variables were used for the binary logit model out of which 6 variables were significant to affect the adoption of CQPPHMP practices by the coffee farmers whereas none of the explanatory variables for sampled traders were found to be significant in the chi-square analysis except checking quality for price. …”
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Ganancia de peso vivo de terneros lactantes criados bajo dos sistemas de crianza y alimentación, Molinopampa, Perú by Rojas Cruz, Diorman

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Modelos empíricos de fertilización nitrogenada en cebada cervecera by Rausch, Analia Fabiana

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