Search Results - Random variables.

Consistency, variability, and predictability of on-farm nutrient responses in four grain legumes across east and west Africa by Heerwaarden, J. van, Ronner, E., Baijukya, F., Adjei-Nsiah, S., Ebanyat, P., Kamai, N., Woldemeskel, Endalkachew, Vanlauwe, Bernard, Giller, Kenneth E.

“…Linear mixed models were used to quantify both mean nutrient responses and their variability, followed by a random forest analysis to determine the extent to which such variability can be explained or predicted by geographic, environmental or farm survey data. …”
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Smallholder coffee farmers’ perceptions of climate variability and the adoption intensity of climate-smart agriculture technologies in Uganda by Kirungi, D., Wesana, J., Sseguya, H., Gellynck, X., De Steur, H

“…A survey was conducted with 226 randomly selected coffee farming households in Luweero district, Uganda. …”
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Phenotyping sunflower genetic resources for Sclerotinia head rot response: assessing variability for disease resistance breeding by Filippi, Carla Valeria, Zubrzycki, Jeremias Enrique, Di Rienzo, Julio A., Quiroz, Facundo Jose, Fusari, Corina Mariana, Alvarez, Daniel, Maringolo, Carla Andrea, Cordes, Diego Darío, Escande, Alberto, Hopp, Horacio Esteban, Heinz, Ruth Amelia, Lia, Veronica Viviana, Paniego, Norma Beatriz

“…Field trials were performed over five consecutive seasons using a twice-replicated randomized complete-block design. Disease incidence, disease severity, incubation period and area under disease progress curve for disease incidence and severity were determined after controlled inoculation with the pathogen. …”
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Agricultura de precisión: dosis variable de fósforo en soja (Glycine max (L.) Merr.) by Peralta, Nahuel Raúl, Trueba, Martín, Wyngaard, Nicolás, Córdoba, Mariano, Sallesses, Leonardo Fabian

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Genetic variability within Phaeoisariopsis griseola from Central America and its implications for resistance breeding of common bean by Mahuku, George S., Jara, Carlos E., Cuásquer, Juan B., Castellanos, G.

“…The genetic and virulence variability of 112 isolates of Phaeoisariopsis griseola, collected from various locations in Central America, were studied using seven random amplified polymorphic DNA (RAPD) primers and 12 common-bean differential genotypes. …”
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AFLP assessment of genetic variability in cassava accessions (Manihot esculenta) resistant and susceptible to the cassava bacterial blight (CBB) by Sánchez, G.M., Restrepo, S., Duque E., Myriam Cristina, Fregene, Martin A., Bonierbale, Merideth W., Verdier, Valerie M.

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Flood-tolerant rice reduces yield variability and raises expected yield, differentially benefitting socially disadvantaged groups by Dar, Manzoor H., de Janvry, Alain, Emerick, Kyle, Raitzer, David, Sadoulet, Elisabeth

“…Approximately 30% of the cultivated rice area in India is prone to crop damage from prolonged flooding. We use a randomized field experiment in 128 villages of Orissa India to show that Swarna-Sub1, a recently released submergence-tolerant rice variety, has significant positive impacts on rice yield when fields are submerged for 7 to 14 days with no yield penalty without flooding. …”
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Response of common bean (Phaseolus vulgaris L.) to nitrogen, phosphorus and rhizobia inoculation across variable soils in Zimbabwe by Chekanai, V., Chikowo, Regis, Vanlauwe, Bernard

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Consumption of Iron-Biofortified Beans Positively Affects Cognitive Performance in 18- to 27-Year-Old Rwandan Female College Students in an 18-Week Randomized Controlled Efficacy T... by Murray-Kolb, Laura E., Wenger, Michael J., Scott, Samuel P., Rhoten, Stephanie E., Lung'aho, Mercy G., Haas, Jere D.

“…Methods: A double-blind, randomized intervention study was conducted in 150 women aged 18–27 y with low iron status (ferritin <20 μg/L). …”
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Monitoring spatial-temporal variations of surface areas of small reservoirs in Ghana’s Upper East Region using Sentinel-2 satellite imagery and machine learning by Ghansah, B., Foster, T., Higginbottom, T. P., Adhikari, R., Zwart, Sander J.

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PCR markerbased analysis of wild and cultivated yams (Dioscorea spp.)in Nigeria: genetic relationships and implication for ex situ conservation by Mignouna, H., Abang, Mathew M., Wanyera, N., Chikaleke, V., Asiedu, Robert, Thottappilly, G.

“…Twenty-five selected random decamer and two microsatellite primers were used individually and in combination to generate DNA profiles for each accession of the six Dioscorea species. …”
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Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa by Muthoni, Francis K., Thierfelder, Christian L., Mudereri, B.T., Manda, J., Bekunda, Mateete A., Hoeschle-Zeledon, Irmgard

“…The agronomic data from the long-term CA trials is used together with gridded biophysical and socio-economic variables. A spatially explicit random forest (RF) algorithm was developed. …”
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Integrating strip cropping with grass border enhances soil fertility, maize grain, and nutritional yields in Zimbabwean smallholder farming systems by Madembo, Connie, Baudron, Frédéric, Mashingaidze, Arnold Bray, Murungweni, Chrispen, Bangira, Courage, Muoni, Tarirai, Thierfelder, Christian

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Rainfall variability, soil heterogeneity, and role of trees in influencing maize productivity—the case from an on-station agroforestry experiment in semi-arid Kenya by Julius, N.M., Catherine, M.W., Shem, K., John, N., Fergus, S.

“…The trial was laid out in Randomized Complete Block Design having a mix of Cambisol and Vertisol soils. …”
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Evaluation of genetic variability in four Nigerian locally-adapted chicken populations using major histocompatibility complex-linked LEI0258 microsatellite marker by Oladejo, O.A., Oseni, S.O., Kyallo, Martina M., Domelevo Entfellner, Jean-Baka, Tor, N.E., Tiambo, Christian K., Pelle, Roger

“…Blood samples were randomly collected from 50 mature birds in each population and DNA was extracted and subsequently subjected to PCR, Sanger sequencing, and bioinformatic analysis. …”
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Genetic variability and heritability of Green Super Rice (GSR) lines resistance to Rice Ragged Stunt Virus (RRSV) and Rice Grassy Stunt Virus (RGSV) by Susanto, Untung, Nafisah, Nafisah, Rohaeni, Wage R., Baehaki S.E., Rahman, Sarlan Abdul, Ali, Jauhar

“…The experiment was arranged following randomized complete block design with three replications. …”
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Maize price seasonality in Ethiopia: Does access to improved grain storage technology matter for farmers’ welfare? by Negede, Betelhem M., Voors, Maarten, De Groote, Hugo, Minten, Bart

“…African seasonal price variability for cereals is two to three times higher than price variability on the international reference market. …”
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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, M., Ramírez, D., Turin, C., Schaeffer, S.M., Konkel, J., Ninanya, J., Rinza, J., De Mendiburu, F., Zorogastua, P., Villaorduña, L., Quiroz, R.

“…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 variables using remote sensing data, land-use and land-cover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. …”
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Efecto de la incorporación de compostaje y contenido ruminal vacuno sobre distintas variables evaluadas en suelo y en plantas de tomate by Fernandez, Ignacio David, Lopez, Juan José, Visentini, Luis Emanuel, Lunad Rocha, Maria Jimena

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Modelado y cartografía del almacenamiento de carbono en los suelos de la República Argentina by Gaitán, Juan José, Peri, Pablo Luis, Barral, Maria Paula, Accame, María Florencia, Beltran, Marcelo Javier, Carfagno, Patricia, Colazo, Juan Cruz, Fantozzi, Anabella Giselle, Kloster, Nanci Soledad, Kurtz, Ditmar Bernardo, Mansilla, Natalia, Moretti, Lucas Martin, Murray, Francisco, Paredes, Paula Natalia, Restovich, Silvina Beatriz, Salvagiotti, Fernando, Saracco, Florencia Soledad, Viana, Horacio Jose Augusto, Von Wallis, Alejandra, Wilson, Marcelo German, Alaggia, Francisco Guillermo, Albarracin Franco, Silvia, Alvarez Cortes, Deivid Joan, Alvarez, Claudio, Bacigaluppo, Silvina, Balducci, Ezequiel Diego, Oliva, Gabriel Esteban, Opazo, Walter Javier, Ybarra, Diego Daniel

“…Este trabajo actualiza el mapa de almacenamiento de COS de Argentina para la capa 0-30 cm, utilizando un enfoque de Mapeo Digital de Suelos con el algoritmo Random Forest. Se analizaron 5.377 sitios mues treados entre 2015 y 2022 y se emplearon 38 variables predictoras climáticas, topográficas, edáficas y de vegetación. …”
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