Resultados de búsqueda - Random variables.

A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems por Hanade Houmma, I., Gadal, S., El Mansouri, L., Garba, M., Gbetkom, P.G., Mamane Barkawi, M.B., Hadria, R.

“…Then, random forest algorithm was used to determine the weights of each component of the model by considering interannual fluctuations in cereal yields as an impact variable. …”
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Genotype and agronomic management interaction to enhance wheat yield and water use efficiency in the Mediterranean rainfed environment of Morocco: I. Field data analysis por Devkota Wasti, Mina Kumari, Devkota, Krishna, Karrou, Mohammed, Nangia, Vinay

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Mapping wetland areas on forested landsacpes using Radarasat-2 and Landsat-5 TM data por Martin, Jennifer

“…The images were classified using two classification methods: the Maximum Likelihood Classifier and Random Forests classifier. Lastly, SAR polarimetric variables and decompositions were investigated for their usefulness in classification. …”

Determinants of rural farmers improved soybean adoption decisions in northern Nigeria por Ojiako, I.A., Manyong, Victor M., Ikpi, A.E.

“…However, the effect of a one-percent increase in each variable is higher for the Tobit than for the logit adoption elasticities. …”
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Unravelling drivers of high variability of on-farm cocoa yields across environmental gradients in Ghana por Asante, Paulina, Rozendaal, Danae, Rahn, Eric, Zuidema, Pieter A., Quaye, Amos, Asare, Richard, Läderach, Peter R.D., Anten, Niels

“…Mixed-effects models showed that the fixed effects (i.e., environmental variables) only explained 7% of the variability in yields whilst fixed and random effects together explained 80%, suggesting that farm-to-farm variation played a large role. …”
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Untangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random forest por Garnaik, Saheed, Samant, Prasanna Kumar, Mandal, Mitali, Mohanty, Tushar Ranjan, Dwibedi, Sanat Kumar, Patra, Ranjan Kumar, Mohapatra, Kiran Kumar, Wanjari, Ravi H., Sethi, Debadatta, Sena, Dipaka Ranjan, Sapkota, Tek Bahadur, Nayak, Jagmohan, Patra, Sridhar, Parihar, Chiter Mal, Nayak, Harisankar

“…At first, a conditional random forest model was built, based on which important variables were selected using the permutation-based variable importance approach. …”
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Variability and synchronism of leaf appearance and leaf elongation rates of eleven contrasting rice genotypes por Egle, Rohilyn B., Domingo, Abigail J., Bueno, Crisanta, Laurena, Antonio, C., Aguilar, Edna A., Santa Cruz, Pompe, Clerget, Benoit

“…Forty four 13-L pots were sown with one plant from one genotype and laid out in 4 randomized complete blocks. The experiment, conducted inside a greenhouse, was repeated twice. …”
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Genetic variability, heritability and genetic advance studies in topcross and three-way cross maize (Zea mays L) hybrids por Sesay, S., Ojo, D., Ariyo, O.J., Meseka, S.

“…Understanding the genetic variability, heritability and genetic advance of traits in any plant population is an important pre-requisite for selection program. …”
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Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables por Velez, Andres Felipe, Alvarez, Cesar Ivan, Navarro, Fabian, Guzman, Diego, Bohorquez, Martha Patricia, Selvaraj, Michael Gomez, Ishitani, Manabu

“…This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. …”
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Phenotypic variability for root traits in Andean common beans grown with and without aluminum stress conditions por Ambachew, D., Asfaw, A., Blair, Matthew W.

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Climate variability and extremes impact on seasonal occurrence patterns of malaria cases in Senegal [Abstract only] por Jampani, Mahesh, Panjwani, Shweta, Ghosh, Surajit, Sambou, Mame Henriette Astou, Amarnath, Giriraj

“…We performed integrated statistical analysis in combination with machine learning models (random forest, neural network, and bayesian hierarchical models) to evaluate and predict the probability of occurrence of malaria cases with respect to regional climate variability and extremes. …”
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Modeling relationships between iron status, behavior, and brain electrophysiology: Evidence from a randomized study involving a biofortified grain in Indian adolescents por Wenger, Michael J., Murray-Kolb, Laura E., Scott, Samuel P., Boy, Erick, Haas, Jere D.

“…Subjects consumed 2 meals/day for 6 months; half were randomly assigned to consume meals made from a standard grain (pearl millet) and half consumed meals made from an iron biofortified pearl millet (BPM). …”
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Opportunities to close wheat yield gaps in Nepal’s Terai: Insights from field surveys, on-farm experiments, and simulation modeling por Devkota Wasti, Mina Kumari, Devkota, Krishna, Paudel, Gokul Prasad, Krupnik, Timothy, James McDonald, Andrew

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Variable returns to fertilizer use and its relationship to poverty: Experimental and simulation evidence from Malawi por Harou, Aurélie, Liu, Yanyan, Barrett, Christopher B., You, Liangzhi

“…Despite the rise of targeted input subsidy programs in Africa over the last decade, several questions remain as to whether low and variable soil fertility, frequent drought, and high fertilizer prices render fertilizer unprofitable for large subpopulations of African farmers. …”
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Spatial variability and vitality of epigeous termite mounds in pastures of Mato Grosso Do Sul, Brazil por Lima, S.S., Ceddia, M.B., Zuchello, F., Aquino, A.M. de, Mercante, F.M., Alves, Bruno J.R., Urquiaga, S., Martius, C., Boddey, Robert M.

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Enabling sesame farmers through the provision of market information and collective action por Kassie, Girma T., Asnake, Woinishet, Worku, Yonas, Abate, Gashaw T., Mesfin, Selamawit, Minot, Nicholas

“…This study aims to assess the effect of market information (MI) and collective action (CA) on sesame production, marketing, and behavioral outcome variables in northwest Ethiopia. We conducted an individually randomized group-treatment trial in which 1560 farm households were randomly divided into three groups: control, market information only (MI), and market information and training in collective action (MICA). …”
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Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India por Nayak, Harisankar, Silva, ‪João Vasco, Parihar, Chiter Mal, Krupnik, Timothy J., Sena, Dipaka Ranjan, Kakraliya, S., Jat, Hanuman Sahay, Sidhu, Harminder Singh, Sharma, Parbodh Chander, Jat, Mangi Lal, Sapkota, Tek Bahadur

“…Random forest was then used for selection of important variables and interactions. …”
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Seasonal and spatial distribution of fall armyworm larvae in maize fields: implications for integrated pest management por Zanzana, K., Sinzogan, A., Tepa-Yotto, G., Dannon, E., Goergen, G., Tamò, Manuele

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Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging por Urquizo Barrera, Julio Cesar, Ccopi Trucios, Dennis, Ortega Quispe, Kevin, Castañeda Tinco, Italo, Patricio Rosales, Solanch, Passuni Huayta, Jorge, Figueroa Venegas, Deyanira, Enriquez Pinedo, Lucia, Ore Aquino, Zoila, Pizarro Carcausto, Samuel

“…Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). …”
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A community-based cluster randomised controlled trial to evaluate the effectiveness of different bundles of nutrition-specific interventions in improving mean length-for-age z scor... por Billah, Masum, Ferdous, Tarana E., Karim, Mohd Anisul, Dibley, Michael J., Raihana, Shahreen, Moinuddin, Md., Choudhury, Nuzhat, Ahmed, Tahmeed, Hoque, D. M. Emdadul, Menon, Purnima, El Arifeen, Shams

“…From a rural district, 125 clusters were selected and randomly assigned to any one of the five study arms by block randomisation. …”
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