scholarly journals Methods of Soybean Genotypes Selection in Paraná State, Brazil

2019 ◽  
Vol 11 (2) ◽  
pp. 100
Author(s):  
Lorena M. Lara ◽  
Michel Esper Neto ◽  
Hugo Zeni Neto ◽  
Alessandro L. Braccini ◽  
Fernanda B. G. Anghinoni ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Growing Season ◽  
Northern Region ◽  
Regression Method ◽  
Segmented Regression ◽  
Genetic Characteristics ◽  
Soybean Crop ◽  
Soybean Cultivars ◽  
Soybean Genotypes ◽  
Weight Number

The soybean crop presents several cultivars available. The performance of each cultivar in the field is associated with its genetic characteristics and the interaction of these with environment. Specific recommendations according to environment are made soybean cultivars release based on adaptability and stability analyzes. This research evaluated twelve soybean cultivars in the northern region of Paraná State Brazil, in order to recommend the most suitable and stable cultivar. The experiment was designed in complete randomized blocks four sites: Maringá, Floresta, Cambé and Apucarana, with four replications, in 2017/2018 growing season. Totaling 48 experimental units per site, that is, a total of 192 in the experiment. The variables evaluated were: one thousand grain mass, productivity, hectoliter weight, number of pods per plant and number of grains per plant. The cultivars were evaluated for adaptability and stability by the methodologies proposed by Lin and Binns (1988) and a bi-segmented regression method according to Cruz et al. (1989). The results indicated that the selection was more reliable when the two methodologies were used, due to their correlation coefficients. Soybean cultivar 3 presented promising behavior in regions studied.

scholarly journals Water depths for different soybean cultivars in center pivot

2018 ◽  
Vol 22 (1) ◽  
pp. 10-15
Author(s):  
Ricardo Gava ◽  
Sebastião F. de Lima ◽  
Osvaldir F. dos Santos ◽  
Jefferson L. Anselmo ◽  
Mayara F. Cotrim ◽  
...  
Keyword(s):  
Climate Change ◽  
Growing Season ◽  
Crop Evapotranspiration ◽  
Genetic Characteristics ◽  
Rainfed Condition ◽  
Soybean Cultivars ◽  
Dry Spells ◽  
Center Pivot ◽  
The One ◽  
Water Depths

ABSTRACT Reductions in soybean yields are mainly linked to the occurrence of dry spells, which are becoming more frequent due to climate change. Thus, the aim of this study was to evaluate soybean cultivars irrigated with different water depths under center pivot. The experiment was conducted during the 2014/15 growing season, under center-pivot irrigation in Chapadão do Sul, MS, Brazil. The design was a randomized block in split plot with four replicates, where plots consisted of 4 water depths (0, 50, 75 and 100% of crop evapotranspiration - ETc) and the subplots of 6 soybean cultivars (NA 5909 RR, DM 5958 IPRO, Anta 82 RR, M 7110 IPRO, Desafio RR, M 7739 IPRO). The cultivar Desafio RR was the one that best responded to irrigation, increasing yield to 6174 kg ha-1 against 3798 kg ha-1 under rainfed condition. The highest yield under rainfed condition was obtained by the cultivar NA 5909 RR, reaching 4806 kg ha-1. This shows that some cultivars can respond positively to irrigation, resulting in increased yield, depending on the genetic characteristics of each one.

Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method

2018 ◽  
Vol 6 (6) ◽  
pp. 119
Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum
Keyword(s):  
Linear Regression ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Regression Method ◽  
Linear Regression Method ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Statistical Parameters ◽  
Time Duration ◽  
Rainfall Prediction

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.

CHANGES IN THE HYDROTHERMAL COEFFICIENT AND IN THE FREQUENCY OF EXTREME HUMIDIFICATION CONDITIONS ON THE TERRITORY OF BELARUS DURING CLIMATE WARMING

2020 ◽  
pp. 5-18
Author(s):  
Yuliya A. Brovka ◽  
Ivan V. Buyakov
Keyword(s):  
Climate Warming ◽  
Growing Season ◽  
Northern Region ◽  
Meteorological Station ◽  
Spatial Change ◽  
Hydrothermal Coefficient ◽  
Arid Conditions ◽  
Moisture Supply ◽  
Moisture Conditions ◽  
Heat And Moisture

It is important to study the heat and moisture supply of the territory under climate warming conditions in Belarus since 1989, as well as changes in the occurrence frequency of extreme moisture conditions. The features of the spatial change in the averaged hydrothermal coefficient (HTC) for the period of climate warming (1989–2019) and the colder period preceding it (1960–1988) in the months of the growing season were revealed based on the maps constructed by interpolation. A decrease in the aridity of conditions in May and September in the southeast and east of the country, an increase in aridity in June and August (especially in the southern regions), an increase in the area with excessive moisture in July and its decrease in September were defined. The article shows the features of changes in the frequency of droughts (HTC ≤ 0,7), less arid conditions (HTC = 0,71–1,0) and excessive moisture (HTC > 1,6) from May to September in 1989–2019, according to compared with the period 1960–1988. It was found that during the period of climate warming from May to September, there is a significant increase in the droughts frequency at meteorological stations in various regions of Belarus. A decrease in the number of years with drought in May and June is observed at several eastern and southern meteorological stations, in August – at the Zhitkovichi meteorological station, in September – at the Kostyukovichi meteorological station. The frequency of arid conditions in May, July and September decreases at many meteorological stations, and its changes are characterized by territorial heterogeneity. An increase in the number of years with less arid conditions is observed in most of Belarus in June and August. A significant increase in the frequency of excessive moisture was noted in July in most of the territory of Belarus, in May – in some regions. A decrease in the frequency of excessive moisture is observed at many meteorological stations in June and August; the number of years with excessive moisture increases only in the northern region. Spatial heterogeneity and less pronounced changes in the frequency of excessive moisture are noted in September.

Growth and Development of Dinitroaniline-Susceptible and -Resistant Goosegrass (Eleusine indica) Biotypes Under Noncompetitive Conditions

Weed Science ◽  
1986 ◽  
Vol 34 (5) ◽  
pp. 704-710 ◽  
Author(s):  
Tim R. Murphy ◽  
Billy J. Gossett ◽  
Joe E. Toler
Keyword(s):  
Growth And Development ◽  
Growth Parameters ◽  
Flag Leaf ◽  
Dry Weight ◽  
Growing Season ◽  
Leaf Length ◽  
Relative Growth ◽  
Eleusine Indica ◽  
Flag Leaf Length ◽  
Weight Number

A comparative study of the growth and development of several populations of dinitroaniline-susceptible (DS) and -resistant (DR) goosegrass [Eleusine indica(L.) Gaertn. # ELEIN] biotypes was conducted under noncompetitive field conditions. Plant height and width, number of tillers, aboveground dry weight, relative growth rate (RGR), number of inflorescences/plant, total inflorescence dry weight, individual inflorescence dry weight, number of spicate branches/inflorescence, and flag leaf length and width were recorded periodically during the growing season. There were no significant differences between biotypes in most characteristics with the exception of total inflorescence dry weight. The DS biotype produced more total inflorescence dry weight than the DR biotype at 8 and 13 weeks after transplanting. Significant variation among DS and DR populations occurred in 37 and 33 of 56 evaluations, respectively. When variation existed within a biotype, populations exhibited broad ranges of response for the various growth parameters. With the exception of total inflorescence dry weight, DS and DR biotypes generally exhibited similar ranges of variability.

scholarly journals Determination and Assessments the Yield Gap Between the Wheat Yield and Potential Yield in Turkey

2018 ◽  
Vol 6 (10) ◽  
pp. 1339
Author(s):  
Bekir Atar
Keyword(s):  
Climatic Factors ◽  
Wheat Yield ◽  
Growing Season ◽  
Steady Increase ◽  
Yield Gap ◽  
Potential Production ◽  
Potential Yield ◽  
Genetic Characteristics ◽  
Wheat Production ◽  
Actual Yield

Knowing the current and the potential production amount of wheat is essential to meet the growing needs. Yield is determined by many factors. The main factors that limit the potential yield are genetic characteristics, and climatic factors such as rainfall and radiation, and management. Wheat is mostly grown in non-irrigated areas in Turkey. The most significant factors that limit dry agriculture wheat production yield in the Mediterranean climatic type are rainfall and its distribution in the growing season. A steady increase in yield is observed in Turkey in recent years. Average annual wheat production is 20.6 million tons. The potential production in this work is determined as 54 million tons. The gap is about 33 million ton. The actual production is 39% of the potential production. The average yield of the Growing Season Rainfall (GSRF) 500 mm areas is 2.2 t ha-1, and the potential yield is 8.8 t ha-1. The gap between the actual yield and potential yield is quite large. The current yield between the areas (GSRF 500 mm) is very small. So it is difficult to explain the gap just because of the rainfall.

scholarly journals ÉPOCAS DE SEMEADURA E PERÍODOS DE COLHEITA DE SOJA VISANDO PRODUÇÃO DE ÓLEO E PROTEÍNAS

Nativa ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 376
Author(s):  
João Paulo Alves Calçado ◽  
Joênes Mucci Peluzio ◽  
Flávia Lucila Tonani Siqueira ◽  
Guilherme Benko Siqueira ◽  
Flávio Sergio Aferri ◽  
...  
Keyword(s):  
Glycine Max ◽  
Oil Content ◽  
Protein Production ◽  
Development Stage ◽  
Low Latitude ◽  
Soybean Cultivars ◽  
Soybean Genotypes ◽  
Glycine Max L ◽  
Sowing Season ◽  
Harvest Delay

O presente trabalho foi realizado com o objetivo de avaliar os teores de óleo e proteína dos grãos em cultivares de soja em função do retardamento da colheita e verificar a influência da época de semeadura sob condições de baixa latitude. No ano agrícola de 2015/16, foram realizados dois ensaios de competição de cultivares de soja em Palmas-TO, sendo o primeiro instalado em 17 de dezembro de 2015 e o segundo em 04 de janeiro de 2016. O delineamento experimental utilizado em cada época de semeadura foi de blocos casualizados com setenta tratamentos e cinco repetições. Os tratamentos foram dispostos em um esquema de parcelas subdivididas, representados por dez genótipos de soja (PRISMA, TMG 132 RR, M 8647 RR, M 8372 RR, BONUS, RAÇA, ST 820 RR, OPUS, TMG 1180 RR e M 9144 RR), nas parcelas, e por sete estádios de colheita dos grãos (R6, R7, R8, R8+7, R8+14, R8+21 e R8+28 dias), nas subparcelas. Houve efeito das épocas de plantio e dos estádios de colheita nos teores de óleo e proteína dos grãos. As cultivares apresentaram comportamentos diferenciados nas duas épocas de semeaduras, a cultivar M9144 RR obteve um crescente aumento nos teores de proteína na segunda época com retardamento da colheita dos grãos. A partir do estádio R8 de desenvolvimento, o teor de óleo nos grãos de três cultivares foi maior na primeira época de semeadura. Na segunda época, quatro cultivares mostram aumento no teor de óleo a partir de R8.Palavras-chave: Glycine Max L.; retardamento; composição química dos grãos. EPOCH AND AFTER DELAYED OF SOYBEAN HARVEST VISUALIZING OIL AND PROTEIN PRODUCTION  ABSTRACT: The present work was carried out with the objective of evaluate the oil and protein contents of the grains in soybean cultivars as a function of the harvest delay and to verify the influence of the sowing season under conditions in low latitude. In the agricultural year 2015/16, two competition trials of soybean cultivars were carried out in Palmas-TO, the first one being installed on December 17, 2015 and the second on January 4, 2016. The experimental design used at each time were randomized blocks with seventy treatments and five replications. The treatments were arranged in a scheme of subdivided plots, represented by ten soybean genotypes (PRISMA, TMG 132 RR, M 8647 RR, M 8372 RR, BONUS, RAÇA, ST 820 RR, OPUS, TMG 1180 RR e M 9144 RR, on the plots, and seven stages of grain harvest (R6, R7, R8, R8+7, R8+14, R8+21 e R8+28 dias) in the subplots. There was an effect of planting times and harvest stages on the oil and protein contents of the grains. The cultivars presented different behaviors in the two sowing seasons; the cultivar M9144 RR obtained an increasing increase in the protein content in the second season with grain harvest delay. From the R8 development stage, the oil content in the grains of three cultivars was higher in the first sowing season. In the second season, four cultivars show an increase in oil content from R8.Keywords: Glycine max L.; retardation; chemical composition of grains.

scholarly journals Forma e tamanho de grãos de soja para diversos teores de água

2018 ◽  
Vol 39 (6) ◽  
pp. 2821
Author(s):  
Wilker Alves Morais ◽  
Osvaldo Resende ◽  
Fernando Nobre Cunha ◽  
Vitor Marques Vidal ◽  
Nelmício Furtado da Silva ◽  
...  
Keyword(s):  
Moisture Content ◽  
Physical Properties ◽  
Surface Area ◽  
Initial Moisture Content ◽  
Correlation Coefficients ◽  
Volumetric Shrinkage ◽  
Linear Regression Models ◽  
Moisture Contents ◽  
Soybean Cultivars ◽  
Grain Moisture

Physical properties of soybean grains present differences as a function of cultivars and moisture content, with the correlation between physical properties. This study aimed to determine the characteristics related to the physical properties of grains with different moisture contents of three soybean cultivars. The experimental design was completely randomized design in a 3 × 6 factorial scheme with three replications, consisting of three soybean cultivars (6266 RSF IPRO, BMX Potência RR, and 14403Z6001) and six grain moisture contents (11, 13, 15, 17, 19, and 21% wb). Soybean grains presented an initial moisture content of 11.0, 11.0, and 10.8% wb, respectively for 6266 RSF IPRO, BMX Potência RR, and 14403Z6001. The other moisture contents were obtained by soaking in a BOD chamber maintained at 25 °C and 93% of relative humidity. We assessed volume, roundness, sphericity, surface area, volumetric shrinkage, and volumetric shrinkage rate. The data were submitted to the analysis of variance by the F-test (p < 0.05) and when significant, regression analysis was performed for grain moisture contents and the means of cultivars were compared by the Tukey’s test. Pearson’s correlation analysis was also carried out to represent the linearity between grain physical properties. The cultivar BMX Potência RR obtained the highest results for volume, roundness, sphericity, and surface area. Volume and surface area increased as the moisture content of soybean grains increased; the opposite was observed for roundness and sphericity. A linear increase in volumetric shrinkage was observed as moisture content increased. The values of the correlation coefficients of the linear regression models can be used to describe the relationships between physical properties.

scholarly journals Prediction of Latitude and Longitude of Earthquakes at Global Level Using the Regressive Objective Regression method

2019 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Correlation Coefficients ◽  
Regression Method ◽  
Careful Attention ◽  
Global Level ◽  
Short Term ◽  
The Real ◽  
Real Value ◽  
Variable Wind ◽  
Objective Methodology ◽  
Significant Model

Objective: To model the series of data of a set of earthquakes at a global level included in the period from 2014-08- 27 23.22.23 UTC until 2014-08-27 04.47.36 UTC, there were 50 earthquakes taken globally and it was determined a model to predict the longitude and latitude of these events. Methods: The Regression Objective Methodology, ROR, is used. Two models are calculated, the first for latitude and the second for longitude, the models are determined in a short term. Results: The correlation coefficients are obtained between the real value and the forecast of 0.716 for model 1 with an error of 25.09 significant degrees at 99% and, for model 2, R = 0.637 with an error of 74.64 degrees significant at 90%. Earthquakes for latitude depends on the value 1.16.12 earthquake behind, the parameter returned 16 steps back are significant at 99%. For this model the trend is 0.118 not significant, model 2 depends on 1,2,11,21 steps back, earthquakes 1 step back is significant at 90%, the trend of the series for model 2 is a rise in length by 4,637 significant at 95%. Discussion: Our work shows the longitude and latitude of earthquakes occurrence are perfectly predictable parameters similar to the variable wind, so a careful attention must be paid to the prevention planes of these phenomena to reduce vulnerability in cities. Conclusions: It is concluded that model 1 is the least error while trend in model 2 has a significant increasing. Global earthquakes are regressive event sat planet level and what happens in one place has repercussions in another, not randomly or due to chance, but rather a well-determined phenomenon.

Rapid and Simultaneous Determination of Three Active Components in Raw and Processed Root Samples of Scutellaria baicalensis by Near-infrared Spectroscopy

Planta Medica ◽  
2018 ◽  
Vol 85 (01) ◽  
pp. 72-80 ◽  
Author(s):  
Xue Jintao ◽  
Yang Quanwei ◽  
Li Chunyan ◽  
Jing Yun ◽  
Wang Shuangxi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Regression Method ◽  
Model Parameters ◽  
Linear Regression Method ◽  
Active Components ◽  
Treatment Methods ◽  
Pre Treatment

AbstractMotivated by the wide use of Scutellariae Radix (SR) in the food and pharmaceutical industries, a rapid and non-destructive near-infrared spectroscopy (NIRS) method was developed for the simultaneous analysis of three main active components in raw SR and SR processed by stir-frying with wine. From seven geographical areas, 58 samples were collected. The reference contents for the SR components baicalin, baicalein, and wogonin were determined by high-performance liquid chromatography. Two multivariate analysis methods, partial least-squares (PLS) regression as a linear regression method and artificial neural networks (ANN) as a nonlinear regression method, were applied to the NIR data, and their results were compared. In the PLS model, different model parameters (i.e., 11 spectral pre-treatment methods), spectral region, and latent variables were investigated to optimize the calibration model; additionally, the ANN model was applied with five different spectral pre-treatment methods and six algorithms. For the optimal model parameters, the correlation coefficients of the calibration set for baicalin, baicalein, and wogonin were 0.9979, 0.9786, and 0.9773, respectively; the correlation coefficients of the prediction set were 0.9756, 0.9843, and 0.9592, respectively; the root mean square error of validation values were 0.215, 0.321, and 0.174, respectively. The optimal NIR models were then employed to analyze the effects of processing and geographical regions on analyte contents. The established NIR methods were robust, accurate, and reproducible. NIRS may be a promising approach for the routine screening and quality control of traditional Chinese medicines.

Variation of morphological indicators of three-line hybrids sunflower and their parental forms

2021 ◽  
pp. 43-54
Keyword(s):  
Plant Growth ◽  
Plant Height ◽  
Correlation Coefficient ◽  
Pollen Fertility ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Growing Season ◽  
Hydrothermal Coefficient ◽  
Average Plant ◽  
Fertility Restorers

The article presents the results of three-year (2016–2018) studies of five three-line hybrids of the IMC selection - Agent, Agronomichny, Marshal, Kamenyar, Zaporozhskij 28 and their parent components - simple unreduced hybrids - ZL22A/102B, ZL42A/46B, ZL42A/58B and pollen fertility restorers – ZL512V, ZL678V and ZL7034V. It was found that individual indicators do not change synchronously. The Agent, Marshal and Kamenyar hybrids had the highest plant height in 2016, one each in 2017 – Agronomichny and in 2018 – Zaporozhskij 28. At the same time, three hybrids (Agent, Agronomical, Marshal) had the largest basket diameter in 2018 and two (Zaporozhskij 28, Kamenyar) in 2017. The shortest growing season hybrids Marshal, Zaporozhskij 28 and Kamenyar had in 2016 (90, 105 and 105 days), and two – Agent and Agronomichny in 2018 – 100 and 103 days, respectively. Among the simple unrecovered hybrids, two – ZL42A/46B and ZL42A/58B had high indicators of plant height, basket diameter and duration of the growing season in 2016. In the ZL22A/102B hybrid, the diameter of the basket was also the largest in 2016 (18.5 cm), the average plant height in 2016 – 124.5 cm was slightly lower than in 2018 (125.4 cm), also in these years in it almost coincided with the duration of the growing season – 97 days in 2016 and 96 in 2018 Fertility restorers had the highest indicators of plant height and basket diameter in 2017, also this year they had the shortest growing season, in 2016, on the contrary, they had the lowest height and the diameter of the basket, and the long growing season. The size of the baskets in the Marshal and Kamenyar hybrids correlates with the moisture supply of plants and the hydrothermal coefficient – the correlation coefficients are 0.997, 0.902 and 0.990, 0.973, respectively. The phases of organogenesis of plants of hybrids Kamenyar and Zaporozhskij 28 are greatly influenced by temperature, the correlation coefficient of this indicator with plant height and with the duration of the growing season is 0.996 for Kamenyar and 0.946 for Zaporozhskij 28, and with a hydrothermal coefficient – 0.939 and 0.753. In the Agent hybrid, the temperatures in June have the greatest influence on plant growth - the correlation coefficient is 0.997 and precipitation in May is 0.968, and the temperatures in May - 0.999 and June - 0.998 on the size of the basket. For the height of plants and the duration of the growing season at Agronomichny, moisture availability at the beginning of the growing season is very important, the correlation coefficients are 0.918 and 0.994, and in August during the filling of seeds 0.996 and 0.927, as well as July precipitation is 0.995. Of the simple unrecovered hybrids, the most demanding for heat is ZL22A/102B - the correlation coefficients are 0.941 with plant height and vegetation duration, 0.843 with the basket size. For ZL42A/58B, the most important are the May precipitation, their correlation with the vegetation duration of 1,000 and the basket diameter of 0.987, and the July temperatures - 0.999 and 0.993, respectively. Among the pollen fertility restorers, the line ZL512V turned out to be the most demanding to weather conditions. For plant growth, the temperatures of April (0.906), May (0.995) and June (1.000) are very important, for the duration of the growing season respectively – 0.958, 0.971 and 0.991, and for the size of the basket, precipitation in May (0.956) and July temperatures (0.943). The correlation coefficient with the sum of active temperatures is – 0.829 for plant height – 0.851 for basket diameter – 0.902 for the growing season.

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