Search Results - Random variables.

Desarrollo de enfermedades en café bajo manejo orgánico y convencional en Paraíso, Costa Rica by Samayoa Juárez, Jorge O.

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Producción de soya (Glycine max (L.) Merril) y frijol (Phaseolus vulgaris (L.) comparada con el método de parcelas by Carmen C., Horacio, Bastidas R., Gilberto

“…The experiment included 16 lines of dry beans and 25 of soybean. A randomized complete block design was used with four replications. …”
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Identifying potential for rice expansion in Burkina Faso: integrating EO and climate data for suitability mapping by Meier, J., Hirner, A., Akpoti, Komlavi, Hackman, K., Gessner, U.

“…Over the last years, food security in West Africa has been strongly influenced by increasing weather variability, including rising temperatures, irregular precipitation patterns, and more frequent extreme events. …”
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A critical synthesis of remote sensing and machine learning approaches for climate hazard impact on crop yield by Obahoundje, Salomon, Tilahun, Seifu A., Zemadim, Birhanu, Schmitter, Petra

“…Statistical approaches, such as the coefficient of variation, remain the dominant methods for analyzing climate variability. For drought detection, Random Forest (RF) was the most used ML algorithm (17%), followed by Support Vector Machines (SVM, 11%), Artificial Neural Networks (ANN, 8%), Adaptive Neuro-Fuzzy Inference System (ANFIS, 5%), and Extreme Gradient Boosting (XGBoost, 5%). …”
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Fertilización nitroazufrada en verdeos invernales puros y consociados con vicias y efectos sobre el suelo by Balbarrey, German Pablo

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Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery by Silveira, Eduarda M.O., Radeloff, Volker C., Martinuzzi, Sebastián, Martinez Pastur, Guillermo J., Bono, Julieta, Politi, Natalia, Lizarraga, Leónidas, Rivera, Luis O., Ciuffoli, Lucía, Rosas, Yamina M., Olah, Ashley M., Gavier Pizarro, Gregorio Ignacio, Pidgeon, Anna M.

“…We analyzed 3788 forest inventory plots (1000 m2 each) from Argentina's Second Native Forest Inventory (2015–2020) to develop predictive random forest regression models. From Sentinel-1, we included both VV (vertical transmitted and received) and VH (vertical transmitted and horizontal received) polarizations and calculated 1st and 2nd order textures within 3 × 3 pixels to match the size of the inventory plots. …”
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Producción y calidad del forraje de alfalfa con gramíneas megatérmicas en el año de implantación = Forage production and quality of warm season grasses associated with alfalfa in t... by Fontana, Laura María Celia, Ruiz, Maria De Los Angeles, Blain, Gabriel, Babinec, Francisco Jose, Romero, Nestor Adolfo

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Förekomst av dubbeltopp i två odlingsmaterial av gran i södra Sverige by Gusthalin, Markus

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Enmiendas y microorganismos antagonistas para el manejo de Pseudomonas solanacearum en tomate by Díaz B, J.U., Bustamante Rojas, Elkin, Sánchez G, V., Schlonvoigt, A.

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Influencia de las fases lunares en la sobrevivencia, enraizamiento de estacas y calidad morfológica de plántulas de Aloysia citrodora = Influence of moon phases on the survival, r... by Balsamo, Maricel, May Petroff, Noelia Judith

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Contenido y digestibilidad in vitro de la fibra de láminas de Chloris gayana Kunth en relación a la edad y tamaño foliar by Avila, Roxana Edith

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Effect Of Uptake Of Climate Information Services On Food Crops Productivity In Rwanda by Laetitia, Irandagiye

“…Suitable climate forecasts would undoubtedly help farmers to respond proactively to threats generated by climate variability. The main objective of this study was to assess the effect of uptake of climate information services on food crops productivity in Rwanda. …”
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Smallholder farms and the potential for sustainable intensification by Mungai, L.M., Snapp, Sieglinde S., Messina, J.P., Chikowo, Regis, Smith, A., Anders, E., Richardson, R.B., Li, G.

“…We present findings from a survey of 324 farmers, located within four Africa RISING sites selected in a stratified random manner to represent (1) low agricultural potential (high evapotranspiration, variable rainfall), (2) medium agricultural potential (two sites), and (3) high agricultural potential (well-distributed rainfall). …”
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Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods by Bakare, M.A., Kayondo, S.I., Aghogho, C.I., Wolfe, M., Parkes, Elizabeth Y., Kulakow, Peter A., Egesi, Chiedozie N., Rabbi, I.Y., Jannink, Jean-Luc

“…Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). …”
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Comparison of UAV and SAR performance for crop type classification using machine learning algorithms: a case study of humid forest ecology experimental research site of west Africa by Duke, O.P., Alabi, T.R., Neeti, N., Adewopo, Julius

“…We also appraise the impact of variable spatial resolution on classification accuracy. …”
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Genetic diversity and within-breed variation in three indigenous Ethiopian sheep based on whole-genome analysis by Asmare, Sisay, Alemayehu, Kefyalew, Mwacharo, Joram, Haile, Aynalem, Guangul, Solomon, Ahbara, Abulgasim

“…The smaller population size, closed breeding system, genetic drift and uncontrolled (non-random) mating might lead to higher rate of inbreeding in Gumuz, Rutana and Washera sheep, requiring timely intervention. …”
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Role of agricultural commercialization in the agricultural transformation of Ethiopia: Trends, drivers, and impact on well-being by Minot, Nicholas, Warner, James, Aredo, Samson Dejene, Zewdie, Tadiwos

“…Somewhat surprisingly, this increase is not due to a shift in crop mix toward more commercial crops but rather an increase in the degree of commercialization of each crop. Using a correlated random effects model, we find marketed share to be significantly related to age of the head of household, farm size, wealth, distance to road, rainfall, rainfall variability, and region. …”
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High-resolution crop-type mapping in northern Ghana by Alabi, Tunrayo, Muthoni, Francis, Uponi, John, Alabi, William, Oluwaleye, Josiah

“…The classification used random forest (RF) and convolutional neural networks (CNN) algorithms. …”
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Screening for fall armyworm (Spodoptera frugiperda J. E. Smith) resistance in early-maturing tropical maize adapted to sub-Saharan Africa by Abubakar, A.M., Adejumobi, I.I., Oyekunle, M., Bonkoungou, T.O., Muhammad, U., Udah, O., Badu-Apraku, B.

“…The Lme4 R package was used to perform an analysis of variance (ANOVA) using a mixed linear model with environment and replicate as random effect while genotype was kept as fixed effect. …”
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Performance of forest plantations in small and medium-sized farms in the Atlantic lowlands of Costa Rica by Piotto, D., Montagnini, F., Ugalde, L., Kanninen, M.

“…Exotic species had the highest performance variability between sites, while native species showed relatively high growth homogeneity. …”
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