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Registros recuperados: 13
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Amino acid composition of soybean seeds as affected by climatic variables PAB
Carrera,Constanza Soledad; Reynoso,Cora Marcela; Funes,Gustavo Javier; Martínez,María José; Dardanelli,Julio; Resnik,Silvia Liliana.
The objective of this work was to perform a quantitative analysis of the amino acid composition of soybean seeds as affected by climatic variables during seed filling. Amino acids were determined from seed samples taken at harvest in 31 multi-environment field trials carried out in Argentina. Total amino acids ranged from 31.69 to 49.14%, and total essential and nonessential amino acids varied from 12.83 to 19.02% and from 18.86 to 31.15%, respectively. Variance components expressed as the percentage of total variation showed that the environment was the most important source of variation for all traits, followed by the genotype x environment interaction. Significant explanatory linear regressions were detected for amino acid content regarding: average...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Glycine max; Environmental variation; Multiple linear regression; Multi-environment trials; Protein composition.
Ano: 2011 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2011001200001
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Background levels of some trace elements in weathered soils from the Brazilian Northern region Scientia Agricola
Fadigas,Francisco Souza; Amaral Sobrinho,Nelson Moura Brasil do; Anjos,Lucia Helena Cunha dos; Mazur,Nelson.
Soils formed from the Barreiras Group sediments, located mainly along the coast of Brazil Northern and Northeastern regions, generally present low concentrations of iron oxides and total organic carbon, high quantities of quartz in the sand fraction, and kaolinitic clay mineralogy. The objective of the present study was to quantify the pseudo total concentrations of Cd, Co, Cu, Cr, Mn, Ni, Zn and Fe in Xhantic Udox and Xhantic Udult soils derived from these sediments. The reference sites were covered by native vegetation and located in the States of Pará and Amapá, Brazil. Multiple linear regression analysis was applied to determine correlations between soil parameters and the levels of these metals. The best correlation was obtained between Fe, Mn, clay,...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Udox; Udult; Heavy metals; Metal soil estimation; Multiple linear regression.
Ano: 2010 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000100008
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Building predictive models of soil particle-size distribution Rev. Bras. Ciênc. Solo
Samuel-Rosa,Alessandro; Dalmolin,Ricardo Simão Diniz; Miguel,Pablo.
Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Digital soil mapping; Terrain attributes; Multiple linear regression; Cross-validation; Additive log-ratio.
Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832013000200013
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Bulk Density Prediction for Histosols and Soil Horizons with High Organic Matter Content Rev. Bras. Ciênc. Solo
Beutler,Sidinei Julio; Pereira,Marcos Gervasio; Tassinari,Wagner de Souza; Menezes,Michele Duarte de; Valladares,Gustavo Souza; Anjos,Lúcia Helena Cunha dos.
ABSTRACT Bulk density (Bd) can easily be predicted from other data using pedotransfer functions (PTF). The present study developed two PTFs (PTF1 and PTF2) for Bd prediction in Brazilian organic soils and horizons and compared their performance with nine previously published equations. Samples of 280 organic soil horizons used to develop PTFs and containing at least 80 g kg-1 total carbon content (TOC) were obtained from different regions of Brazil. The multiple linear stepwise regression technique was applied to validate all the equations using an independent data set. Data were transformed using Box-Cox to meet the assumptions of the regression models. For validation of PTF1 and PTF2, the coefficient of determination (R2) was 0.47 and 0.37, mean error...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Pedotransfer functions; Multiple linear regression; Box-cox transformation; Soil database.
Ano: 2017 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832017000100309
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Crop Diversification in Karnataka: An Economic Analysis AgEcon
Acharya, Saraswati Poudel; Basavaraja, H.; Kunnal, L.B.; Mahajanashetti, S.B.; Bhat, Anil R.S..
The nature and extent of crop diversification in the Karnataka state has been analyzed by collecting secondary data for a period of 26 years from 1982-83 to 2007-08. Composite Entropy Index (CEI) and multiple linear regression analysis have been used to analyze the nature and extent of crop diversification in the state. The CEI for different crop groups has shown that almost all the crop groups have higher crop diversification index during post-WTO (1995-96 to 2007-08) than during pre-WTO (1982-83 to 1994-95) period, except for oilseeds and vegetable crops. There has been a vast increase in diversification of commercial crops after WTO. Crop diversification is influenced by a number of infrastructural and technological factors. The results have revealed...
Tipo: Article Palavras-chave: Crop diversification; Composite entropy index; Multiple linear regression; Karnataka; Agricultural and Food Policy; Q16.
Ano: 2011 URL: http://purl.umn.edu/119408
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Crop yield estimation using satellite images: comparison of linear and non-linear models Agriscientia (Córdoba)
Sayago,S; Bocco,M.
Development of models for crop yield prediction using remote sensing allows accurate, reliable and timely estimations over large areas. Particularly, this information is necessary to ensure the adequacy of a nation's food supply as well as to aid policy makers and farmers. In Argentina, soybean (Glycine max (L.) Merr.) and corn (Zea mays L.) are the most important crops. The goal of this research was to develop and evaluate linear and non-linear models to estimate crop yield from satellite data. Particularly, we proposed and applied those models to obtain soybean and corn yield in the central region of Córdoba (Argentina) using Landsat and SPOT images. The models were designed taking into account all or some bands included in the images from one or both...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Neural networks; Multiple linear regression; Soybean; Corn; Modelling.
Ano: 2018 URL: http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S1668-298X2018000100001
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Egg hatchability prediction by multiple linear regression and artificial neural networks Rev. Bras. Ciênc. Avic.
Bolzan,AC; Machado,RAF; Piaia,JCZ.
An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial incubation; Artificial neural networks; Hatchability; Multiple linear regression.
Ano: 2008 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2008000200004
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Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions Rev. Bras. Ciênc. Solo
Carvalho Junior,Waldir de; Chagas,Cesar da Silva; Lagacherie,Philippe; Calderano Filho,Braz; Bhering,Silvio Barge.
Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Multiple linear regression; Kriging; Co-Kriging.
Ano: 2014 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832014000300003
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Oat yield through panicle components and growth regulator AGRIAMBI
Marolli,Anderson; Silva,José A. G. da; Mantai,Rubia D.; Brezolin,Ana P.; Gzergorczick,Maria E.; Lambrecht,Darlei M..
ABSTRACT The growth regulator modifies the expression of lodging and panicle components in oat plants, with reflexes in yield. The objective of this study was to define the optimal dose of growth regulator in oat for a maximum lodging of 5%. In addition, this study aimed to identify potential variables of the panicle to compose the multiple linear regression model and the simulation of grain yield in conditions of use of the regulator under low, high and very high fertilization with nitrogen. The study was conducted in 2011, 2012 and 2013 in a randomized block design with four replicates in a 4 x 3 factorial scheme, for growth regulator doses (0, 200, 400 and 600 mL ha-1) and N-fertilizer doses (30, 90 and 150 kg ha-1), respectively. The growth regulator...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Avena sativa; Nitrogen; Trinexapac-ethyl; Multiple linear regression.
Ano: 2017 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662017000400261
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Satellite Spectral Data on the Quantification of Soil Particle Size from Different Geographic Regions Rev. Bras. Ciênc. Solo
Demattê,José Alexandre Melo; Guimarães,Clécia Cristina Barbosa; Fongaro,Caio Troula; Vidoy,Emmily Larissa Felipe; Sayão,Veridiana Maria; Dotto,André Carnieletto; Santos,Natasha Valadares dos.
ABSTRACT: The study of soils, including their physical and chemical properties, is essential for agricultural management. Soil quality must be maintained to ensure sustainable production of food and conservation of natural resources. In this context, soil mapping is important to provide spatial information, which can be performed using remote sensing (RS) techniques. Modeling through use of satellite data is uncertain regarding the amplitude of replicability of the models. The aim of this study was to develop a quantification model for soil texture based on reflectance information from a continuum of bare soils, obtained by overlapping multi-temporal satellite images, and apply this model to an unknown region to evaluate its applicability. Spectral data...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil texture; Remote sensing; Bare soil mask; Multiple linear regression; Digital soil mapping.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100310
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Spatial prediction of soil properties in two contrasting physiographic regions in Brazil Scientia Agricola
Menezes,Michele Duarte de; Silva,Sérgio Henrique Godinho; Mello,Carlos Rogério de; Owens,Phillip Ray; Curi,Nilton.
ABSTRACT This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Ordinary kriging; Multiple linear regression; Regression kriging.
Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000300274
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The use of Pedotransfer functions and the estimation of carbon stock in the Central Amazon region Scientia Agricola
Gomes,Andréa da Silva; Ferreira,Ana Carolina de Souza; Pinheiro,Érika Flávia Machado; Menezes,Michele Duarte de; Ceddia,Marcos Bacis.
ABSTRACT Computer models have been used to assess soil organic carbon (SOC) stock change. Commonly, models require to determine soil bulk density (Db), a variable that is often lacking in soil data bases. To partly overcome this problem, pedotransfer functions (PTFs) are developed to estimate Db from other easily available soil properties. However, only a few studies have determined the accuracy of these functions and quantified their effects on the final quality of the spatial variability maps. In this context, the objectives of this study were: i) to develop one PTF to estimate Db in soils of the Brazilian Central Amazon region; ii) to compare the performance of PTFs generated with three other models generally used to estimate Db in soils of the Amazon...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Içá Formation; Multiple linear regression; Ordinary kriging.
Ano: 2017 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162017000600450
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由植被高解析反射光譜模式化稻株之生長 Taiwan Agricultural Research Institute
楊純明; 陳榮坤; Chwen-Ming Yang; Rong-Kuen Chen.
[[abstract]]本文研究旨在探討近地面量測之水稻植被高解析反射光譜之季節變化,並試以利用不同方法來篩檢估測水稻生長的光譜特徵並建立光譜遙測模式。高解析光譜係以田間攜帶式高解析輻射光譜儀偵測,生長性狀則於量測光譜時取樣調查,試驗期間為2000-2002 年之一二期稻作,計有四期作。篩檢之光譜特徵為可見光之綠光波段峰點(G REEN)、紅光波段之谷點(RED)及近紅外光之波段頂點(NIR),而光譜指數係由此三項動態特徵之反射比計算,包括RRED/RNIR ratio、RGREEB/RNIR ratio、RRED/RGEEN ratio及NDVI (normalized difference vegetation index,標準差植被指數或稱正規差植生指數)。試驗發現稻株之生長性狀於抽穗前後達到最高點,再隨著成熟老化而下降。據此,將水稻生育全期以抽穗為分割點劃分為抽穗前期(pre-heading phase )及抽穗後期(post-heading phase ) ,可提高光譜特徵及光譜指數與生長性狀間之相關性。又由多元直線複迴歸(MLR)分析,可經由對決定係數(coefficient of determination , R2的要求,來選取光譜中合適的光譜特徵波段數目以建立多元直線複迴歸模式估測稻株之生長。根據研究結果,多元直線複迴歸模式確實提供了選取光譜特徵的彈性,同時也改進了對稻株生長變異的估測。 Experiments were conducted to study the seasonal changes of rice canopy reflectance spectra from near ground platform and to modeling rice (Oryza sativa...
Palavras-chave: 高解析反射比光譜; 水稻生長模式化; 直線複迴歸; 光譜特徵; 光譜指數 Hyperspectral reflectance spectrum; Rice growth modeling; Multiple linear regression; Spectral characteristic; Spectral index 水稻精準農業體系 Rice Precision Farming System [[classification]]6.
Ano: 2003
Registros recuperados: 13
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