ABSTRACT: In order for sunflower cultivation to be economically sustainable, research should be based upon suitable experimental techniques. Since this kind of information is not readily available, the aim of this study was to estimate the seed production heterogeneity index and the optimal experimentalplotsize, and to verify experimentalprecision in sunflower experiments. Sunflower seed yield figures for plots of 1-meter rows (0.4 m spacing) were collected. The experiments were carried out in the 2004/05 and 2005/06 growing seasons in a 1-ha area, by marking out 12 randomized blocks (12 uniformity trials) of two rows 48 plots long on land used for commercial production of sunflowers in Bossoroca, state of Rio Grande do Sul, Brazil. Plots of different sizes were simulated and estimates made for the mean, variance and coefficient of variance for each plotsize, and the production heterogeneity index, optimal plotsize and experimentalprecision estimated. The sunflower seed production heterogeneity index was high, the plots should be large and the rows are the blocks. The optimal plotsize is two 3-meter rows (2.4 m 2 ).
combinations of experimental designs, coefficients of variation, numbers of treatments and repetitions.
Alternatively, it is possible to estimate the coefficient of variation (CV) among basic experimental units and the soil heterogeneity index (b) of Smith (1938). Thereafter, use the Hatheway’s method (1961) with the aim of determining the Xo according to the experimental design, number of treatments, number of repetitions, and desired precision. This approach, with different plotsize estimates has advantages over the previous one, which has a unique optimum plotsize. In the second approach, once the experimental design and the number of treatments to be evaluated are established, the researcher can choose the best combination of plotsize, number of repetitions, and experimentalprecision levels. This approach has been used for traits in crops such as cassava (Viana et al., 2003), maize (Alves & Seraphin, 2004), wheat (Henriques Neto et al., 2004), and bean (Mayor- Durán et al., 2012).
In experiments with 50 treatments and higher experimentalprecision (d = 10%), the required number of replications is the same in the completely randomized design and in the randomized blocks design, i.e., 69.96 and 76.72 replications for the measurement of fresh and dry matter, respectively. Thus, due to the high number of replications these experiments are not feasible. Nevertheless, whoever uses the information of this study can choose the combination of experimental design, number of treatments, minimal differences among the treatment means and the number of replications suitable to carry out one’s experiment (Tables 2, 3, 4 and 5). It is important to note that the information provided on this study is from the Xo and CVXo defined according to Paranaiba et al. (2009) methodology. Although the gain in the accuracy from the increasing of the Xo is not very expressive, it is possible to opt for the increasing of the Xo to reduce the CVXo, and, thus, improve the experimental accuracy.
select a plot with optimum size for this purpose. This minimum size of an experimentalplot for a given degree of precision is named as Optimum plotsize. Once the optimum plotsize has been determined, we have to decide the shape of le shape of experimental plots is the one that will result in the smallest variation between plots within a block. In agricultural experiments many works on shape of Adjacent areas are correlated was first shown by Harris (1920) and he utilized this criteria for subdividing the field into uniform areas. The use of intra class correlation as a measure of heterogeneity was suggested by him and recommended that if correlation coefficient was in the neighborhood of zero then field could be considered as homogeneous field and whatever plotsize is adopted, it will not lead to a large experimental error. After that as we explore scientific literature relating to this problem, we may a note a number of contributions, including Smith (1938) developed an empirical model representing the relationship between plotsize and variance of mean per plot. Koch & Rigney (1951) demonstrated that the regression coefficient of the logarithm of variance on the logarithm of plotsize can be estimated from rimental data in which treatment effects were present and from uniformity trials. They recommended that the variances of the different plot sizes should be weighted by their respective degrees of freedom. Bhatti and Rashid (2005) studied the pe and size of plots on spatial variability in yields. INTERNATIONAL JOURNAL OF CURRENT RESEARCH
Sugarcane plots 04, 05, 50, 51 and 52, of experi- mental field 1, were proposed as belonging to environ- ment B, while the southern plots, 10, 11 and 12, were classified as environment C (Figure 5C). According to soil pedological classification, plot 09 was divided into environments B and C. For experimental field 2, a new division of sugarcane plots was proposed, dividing the whole field in a north-south direction (Figure 5D). Plot 33 remained within the same previously classified en- vironment, while plot 32 was changed to environment E. The revised plot divisions in field 2 were adequate in the context of the ECa and clay content spatial vari- ability, as observed in the previous maps. From an agronomic perspective, the amended division may be more suitable for crop management compared to the original division. In contrast to experimental field 2, the division of plots was maintained at experimental field 1. It could be observed that environment B was found in the sites with higher clay and ECa contents, corresponding to the sites with the greatest yield po- tential. Despite the number of samples being equal for both classifications for field 1, guided samples by ECa helped to sound out a new soil type and “production environments”.
leaves, dhub grass, banana peelings (Sharma et al., 1988), water hyacinth (Moorhead and Nordstedt, 1993), castor oil cake (Gollakota and Meher, 1988), sunflower oil cake (De la Rubia et al., 2011), food waste (Izumi et al., 2010) and municipal solid waste (Zhang and Banks, 2013). Moreover, extensive analysis of available literature reveals inconsistent reports on effect of lignocellulosic biomass particle size to enhance the sugar yield in hydrolyzate. Some say that smaller size particles produces higher sugar yield, while some say that larger size particles produces higher sugar yield, and some say that particle size does not have effect on sugar yield in hydrolyzate (Zhang et al., 2013). Furthermore, the particle sizes are characterized as coarse, fine, and ultra–fine on the basis of mean particle size. Particles having mean particle diameter ≥800 µm are generally considered as coarse size, and particles <100 µm as fine, and particles <25 µm as ultra–fine. The levels of particle size of biomass determine the level of increase in available surface area as well as mechanical disruption to the lignocellulosic structure of individual biomass, and on other hand the amount of energy required in the grinding process (Gabriela et al., 2012).
It has often been pointed out (for example, Goodall, 1952; Cain & Castro, 1959; Van der M aarel, 1966; Daubenm ire, 1968) that on the species area regression curve the point o f inflexion depends on the relative scales o f abscissa and ordinate axes. Cain & Castro (1959) showed that, depending on the ratio of these axes, they could find three different minimal areas for an American grassland association. They then tried to develop a more accurate and independent method to determine the point of inflexion. A tangent to the curve was constructed, parallel to a line through zero and a point (x, y), where x is 10% o f the ultim ate area that is surveyed, and y is 10% of the number of species for that area. The tangent “ point” gives then the minimal area. This type of method has the great disadvantage, however, as pointed out by Goodall (1952), that the resulting minimal area depends closely on the size o f the largest area that is surveyed—the larger this area, the larger the minimal area.
The test site is situated in the area of the Carpath- ian foothills, Wiśnickie hills mesoregion (Kond- racki 2002). An experimentalplot was established in 8 replications (blocks) in sub-compartments 81d, g, h, i in the Chrostowa forest range of Brzesko Forest District (Fig. 1). The oaks were planted in the system of single-tree plot, and the neighbour- ing individuals were from different provenances and families. Distribution of families on the experi- mental plot was random (different in each of the blocks). Distribution of individual oaks is present-
Students often think of Bode plots as just and easy way to get easy points on an exam by following a bunch of rules, and completely forget the information you get from the Bode plot. For the following two systems, with the frequency response of two transfer functions represented by their Bodes plots, you are to determine the steady state output of the systems for the given inputs.
** Z – one half the differences between the natural logarithms of the 2 variances
The total sum of squares was obtained by squaring the yield of each plot, summing, and subtracting the product of the general total times the general mean. The sum of squares between blocks was obtained by squaring the total yield of each of the 24 blocks, summing dividing by 48 (the number of elements contributing to each total) and subtracting the same product of the general total times the general means as used in obtaining the total sum of squares. The sum of squares due to variation within the blocks is the difference between the total sum of squares and that portion due to variation between blocks. Since a total of 1152 plots were considered, there were 1151 (N-1) degrees of freedom attributable to the total sum of squares. There were 24 blocks (of 48 plots each) and consequently 23 degrees of freedom due to blocks; 1151-23 or 47 x 24 gives 1128 degrees of freedom due to variation between the 48 plots within each of the 24 blocks. The mean squares or variance (standard deviation squared) is found by dividing the sum of squares by the corresponding degrees of freedom.
Grain yield is the ultimate aim of every crop sown for seed in case of seed crops. It is the result of many factors which ultimately related to produce the seed. The data regarding to mungbean grain yield was presented in Table 2. Data show highly significant effect of planting patterns and intercropping on the grain yield. Maximum grain yield 1.12 t ha -1 was found when mungbean was sown alone at 30 cm spacing in case of intercropping. But as far as planting patterns is concerned maximum grain yield of 0.91 t ha -1 was measured in P 3 I 2 (175/35 cm four rows apart planting) followed by P 1 I 2 0.89 t ha -1 (sunflower sowing at 70 cm single row planting in mungbean intercropping) while the minimum grain yield was measured in case of P 2 I 2 0.85 t ha -1 (105/35 cm spaced paired rows sunflower planting in mungbean combination). The reasons of reduction in the intercropped mungbean yield may be lower number of pods per plants, number of seeds per pods, 1000-grain weight. Different suppressive effects of intercropping on various yield components of mungbean grown under different planting patterns may be due to shading effects of sunflower on lower canopy of legume and interspecific competition between mungbean and sunflower. Similar type of results were described by Sultana (2007) where higher seed yield was found mungbean alone cultivation in case of intercropping and maximum grain yield in P 1 (70cm single row planting) and minimum in P 2 (105/35 cm spaced paired row planting) in case of planting geometry. Ahmad and Rao (1982) and Vyas et al. (1995) narrated significant effect of seed yield of intercrops. Bhatti (2005), Khan (2000) and Rao (1991) also described the significant effect of intercropping on mungbean grain yield.
The choice of attributes associated with the number of precision technologies used is guided by human capital theory, farm and production characteristics, and other adoption models. Nelson and Phelps (1980), Khaldi (1979), and Wozniak (1989) use education as a measure of human capital to reflect the ability to innovate (either technology or insurance). In addition, other factors affecting the adoption of precision farming technologies are driven by the literature (Feder et al., 1985; Rogers, 1995; Deberkow and McBride, 2003). In our model, we use financial, location, and the physical attributes of the farm firm that may also influence
Agriculture is the backbone of our country and economy, which accounts for almost 30 per cent of Gross Demand Product (GDP) and employs 70 per cent of the population. Agricultural technology available in the 1940s could not have been able to meet the demand of food for today’s population, in spite of the green revolution. Similarly, it is very difficult to assume that food requirement for the population of 2020 AD will be supplied by the technology of today. To meet the forthcoming demand and challenge we have to divert towards new technologies, for revolutionizing our agricultural productivity. Green revolution succeeded in India to increase the farmer’s income, yield of major crops and made India self-reliant in food production, with the introduction of high- yielding varieties and use of synthetic fertilizers and pesticides (Ghosh et al., 1999).
The sunflower crop has a special significance in our country; therefore, the crop is spread over large areas, being appreciated in areas with difficult climatic conditions. The sunflower fruits (achene) contain a of 50%, oil with exceptional food quality and high conservation degree; they are used in human food (refined) and in the food industry (margarine, cans, soap, lecithin phosphatides, etc.) . The sunflower can be placed in rotation after autumn or spring cereals. It is not recommended sowing sunflower seeds after sugar beets, alfalfa, Sudan grass, as they dry ground strong and deeply. It is not recommended also to sow sunflower after rape, peas, soybeans, beans, as these crops have many common diseases (Sclerotinia, white and grey mold, etc.). The sunflower in crop rotation should lie on the same field not earlier than 7-8 years, in order to prevent the accumulation of soil in the seed of broomrape (Orobanche) and infectious diseases. It is interesting that, by the established eco conditions compliance, the sunflower should not be grown on the same site (plot, sub-plot) for more than 2 consecutive years, because it extracts from soil large amounts of nutrients and it promotes proliferation of some weeds, pests and specific diseases . We believe that the failure of this condition encourages the exhaustion of the soil, in order to achieve some short-term profits by growing large areas of sunflowers. The land preparation is done in autumn or spring by superficial work in order to keep water reserve in the soil. The seedbed is prepared in a single pass with total grower or combiner, before sowing, at a depth of 5-6 cm. The germination capacity; minimum 95%; need for seed - 3.5-5 kg/ha (for an optimal harvest density of 30-50 thousand plants/ha); 5-13 thousand seeds per kg. The age of seed: you have to take into account the water reserve in the soil, the pressure of pests and diseases, weed pressure and used variety. The optimum time of sowing is in spring when the soil at 4-6 cm seeding depth recorded a temperature of 6-7 degrees. The density is determined depending on the biological potential of the variety, quality indexes seed and plant health, sowing time, seedbed quality and pressure of diseases at increased. Early hybrids 52 000-55 000; mid early hybrids 50 000-53 000; tardy hybrids 48 000- 50 000; weight of 1000 grains: 50-80 g. Seed quantity per hectare: 3.5-5 kg. Seeding depth: 4-6 cm. The harvesting begins when seed moisture content is 15-16%. It is not harvested at full maturity due to large losses by shaking. The production can be obtained up to 4000 kg/ha . Following the evolution of grown area (Table 1) with sunflower, an increase of 27% in 2013 compared to 2008.
Treatments consisted of three mulch types (No mulch, rice-straw mulch and black polythene mulch), four nitrogen fertilizer rates (0, 45, 90 and 135 kg/ha -1 ) and three irrigation intervals (5, 10 and 15days). They were arranged in a split plot design with factorial combinations of nitrogen and irrigation in the main plot and mulching in the sub-plot, and replicated three times.
and weighed to estimate yields for experimental lines and commercial cultivars. This harvest was done by hand in the U.S. before the 1970s, and is a common practice in cotton breeding programs in other countries. Since the 1970s until the mid-2000s, mechanical harvesting of seedcotton from small plots has become a reality in cotton breeding in the U.S. A one- or two-row cotton picker with a crew of three or five people was modified to catch seedcotton from each plot in a labeled bag or sack; the filled sack was removed and weighed by two to three people on the ground. The weighed seedcotton was emptied into a cotton wagon or trailer. The emptied bags were cleaned, labeled, and arranged for the cotton picker based on the next harvest order. Therefore, the entire operation was labor intensive and time-consuming, in addition to posing environmental, health, and safety hazards to the crew such as dust and danger from working on the back of the picker.
Résumé – Tournesol (Helianthus annuus L.) en Turquie : ressources génétiques, production et recherche. En Turquie, le tournesol est l’une des principales cultures d’oléagineux et est principalement cultivé dans la région de Thrace. En 2017, les tournesols oléagineux et de bouche ont été cultivées sur 77 9622 hectares en Turquie pour une récolte de 1 964 385 t et des rendements moyens de 2640 t/ha pour le premier et 1 670 t/ha pour le second. La Turquie est l ’ un des principaux pays pour la diversité des cultures et est décrite comme un microcentre pour certaines cultures originaires de différentes régions du monde. Les variétés du tournesol (Helianthus annuus L.) présentent également une diversité importante en Turquie. Les variétés de pays de tournesol ont été rassemblées dans le cadre du « Projet national de ressources génétiques pour les cultures industrielles » (National Industrial Plant Genetic Resources Project, NPGRP). Neuf cent trente-deux entrées (932 entrées) de tournesol oléagineux et de bouche sont en conservation à long terme à la Banque nationale de ressources génétiques de Turquie. Le projet de recherche national sur le tournesol a pour mission de développer du matériel génétique amélioré au moyen de techniques de sélection conventionnelles et biotechnologiques pour la région de Thrace et d’autres zones de production en Turquie. De nouveaux germplasmes et lignées parentales ont été mis au point pour améliorer les variétés hybrides oléagineux et de bouches avec les caractères recherchés : rendement élevé et huile de bonne qualité, résistance aux maladies telles que Plasmopara halstedii (Farl.) Berl de Toni., Puccinia helianthi Schw. et Orobanche cumana Walr. Les comportements en conditions défavorables sont également pris en compte. Ces études sont également intégrées à des recherches en agronomie et d’autres sujets connexes.
Each plot area was 42 m 2 including 10 ridges, 7 m long and 0.60 cm apart. Plots were isolated by ditches of 1.5 m in width to avoid lateral movement of water. Seed of sunflower cultivar Sakha 53 was sown on March 15 th , 2008 and 19 th , 2009 at hills 20 cm apart on one side of the ridges and harvested on July 7 and 17 in both seasons, respectively. In both seasons, phosphorous fertilizer in the form of calcium super phosphate (15.5 % P 2 O 5 ) was applied at the rate of 30
Precision grinding of silicon has been demonstrated for pre- cise formation of silicon diaphragms. Diaphragms 2– 6 mm in diameter and 25–150 m thick were produced. It was observed that the process induces bending in the dia- phragms if they are not supported during grinding. The use of SOI technology can virtually eliminate bending since the diaphragm is always supported by underlying silicon dur- ing the grinding steps, however, the process is less eco- nomical since an additional silicon wafer and a bonding step are required. The use of porous silicon as a support layer has been shown to significantly reduce the amplitude of bending by a factor of up to several hundred. Stress measurements of the diaphragms were performed using Ra- man and x-ray spectroscopies and indicate the existence of compressive stress of the order of 1 ⫻ 10 7 – 1 ⫻ 10 8 Pa in unsupported diaphragms and in those supported by porous silicon, whereas the diaphragms based on SOI technology are stress free. Simulations of the bent diaphragms were performed using 3D FEM analysis. The results for 6 mm diam diaphragms indicate that deterioration of the perfor- mance, in terms of deflection, is negligible for diaphragms with convex bending of ⬍ 10 m.