980 resultados para Biomass, dry mass, standard deviation
(Table 1) Sample information and analytical data for bulk material with standard deviation (1 Sigma)
Resumo:
Water regulations have decreased irrigation water supplies in Nebraska and some other areas of the USA Great Plains. When available water is not enough to meet crop water requirements during the entire growing cycle, it becomes critical to know the proper irrigation timing that would maximize yields and profits. This study evaluated the effect of timing of a deficit-irrigation allocation (150 mm) on crop evapotranspiration (ETc), yield, water use efficiency (WUE = yield/ETc), irrigation water use efficiency (IWUE = yield/irrigation), and dry mass (DM) of corn (Zea mays L.) irrigated with subsurface drip irrigation in the semiarid climate of North Platte, NE. During 2005 and 2006, a total of sixteen irrigation treatments (eight each year) were evaluated, which received different percentages of the water allocation during July, August, and September. During both years, all treatments resulted in no crop stress during the vegetative period and stress during the reproductive stages, which affected ETc, DM, yield, WUE and IWUE. Among treatments, ETc varied by 7.2 and 18.8%; yield by 17 and 33%; WUE by 12 and 22%, and IWUE by 18 and 33% in 2005 and 2006, respectively. Yield and WUE both increased linearly with ETc and with ETc/ETp (ETp = seasonal ETc with no water stress), and WUE increased linearly with yield. The yield response factor (ky) averaged 1.50 over the two seasons. Irrigation timing affected the DM of the plant, grain, and cob, but not that of the stover. It also affected the percent of DM partitioned to the grain (harvest index), which increased linearly with ETc and averaged 56.2% over the two seasons, but did not affect the percent allocated to the cob or stover. Irrigation applied in July had the highest positive coefficient of determination (R2) with yield. This high positive correlation decreased considerably for irrigation applied in August, and became negative for irrigation applied in September. The best positive correlation between the soil water deficit factor (Ks) and yield occurred during weeks 12-14 from crop emergence, during the "milk" and "dough" growth stages. Yield was poorly correlated to stress during weeks 15 and 16, and the correlation became negative after week 17. Dividing the 150 mm allocation about evenly among July, August and September was a good strategy resulting in the highest yields in 2005, but not in 2006. Applying a larger proportion of the allocation in July was a good strategy during both years, and the opposite resulted when applying a large proportion of the allocation in September. The different results obtained between years indicate that flexible irrigation scheduling techniques should be adopted, rather than relying on fixed timing strategies.
Resumo:
Potassium fertilization is very important to alfalfa crop in terms of yield, quality and persistence of forage, especially on soils naturally poor K. Thus, to assess the effects of K fertilization in alfalfa production and nutritional status, was carried out an experiment in a greenhouse using samples of a Dystrophic Oxisol medium texture (LV) (0.6 mmol(c) dm(-3) K) and a Dystrophic Ultisol sandy/medium texture (PVA) (2.2 mmol(c) dm(-3) K). A completely randomized design in a factorial arrangement 6 x 2 (six K rates and two soils) was used, with four replications. The K rates used were: 0, 25, 50, 100, 150 and 200 mg kg(-1) K. Potassium fertilization increased K content in soil and shoots. Dry matter production was increased with the K addition. However, in the PVA, this occurred only in the second cut. In LV, potassium fertilization increased N concentration in alfalfa shoots in both cuts. Plants with K concentration around 10 g kg(-1) had typical symptoms of this nutrient deficiency. The K critical levels of K in soil and shoots were 1.8 mmolc dm(-3) and 16.7 g kg(-1), respectively.
Resumo:
Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^
Resumo:
Seasonal dynamics in the activity of Arctic shelf benthos have been the subject of few local studies, and the pronounced among-site variability characterizing their results makes it difficult to upscale and generalize their conclusions. In a regional study encompassing five sites at 100-595 m water depth in the southeastern Beaufort Sea, we found that total pigment concentrations in surficial sediments, used as proxies of general food supply to the benthos, rose significantly after the transition from ice-covered conditions in spring (March-June 2008) to open-water conditions in summer (June-August 2008), whereas sediment Chl a concentrations, typical markers of fresh food input, did not. Macrobenthic biomass (including agglutinated foraminifera >500 µm) varied significantly among sites (1.2-6.4 g C/m**2 in spring, 1.1-12.6 g C/m**2 in summer), whereas a general spring-to-summer increase was not detected. Benthic carbon remineralisation also ranged significantly among sites (11.9-33.2 mg C/m**2/day in spring, 11.6-44.4 mg C/m**2/day in summer) and did in addition exhibit a general significant increase from spring-to-summer. Multiple regression analysis suggests that in both spring and summer, sediment Chl a concentration is the prime determinant of benthic carbon remineralisation, but other factors have a significant secondary influence, such as foraminiferan biomass (negative in both seasons), water depth (in spring) and infaunal biomass (in summer). Our findings indicate the importance of the combined and dynamic effects of food supply and benthic community patterns on the carbon remineralisation of the polar shelf benthos in seasonally ice-covered seas.
Resumo:
The Sesame dataset contains mesozooplankton data collected during April 2008 in the Levantine Basin (between 33.20 and 36.50 N latitude and between 30.99 and 31.008 E longitude). Mesozooplankton samples were collected by using a WP-2 closing net with 200 µm mesh size during day hours (07:00-18:00). Samples were taken from 0-50, 50-100, 100-200 m layers at 5 stations in Levantine Basin The dataset includes samples analyzed for mesozooplankton species composition, abundance and total mesozooplankton biomass. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling biomass was measured by weighing filters and then determined by sampling volume. The samples were sieved sequentially through meshes of 500 and 200 micron to separate the mesozooplankton into size fractions. The entire sample (1/2) or an aliquot of the taxon-specific mesozooplankton abundance and the total abundance of the mesozooplankton were was analyzed under the binocular microscope. Minimum 500 individuals of mesozooplankton were identified and numerated at higher taxonomic level. Taxonomic identification was done at the METU- Institute of Marine Sciences by Alexandra Gubanova,Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton abundance and biomass were estimated by Zahit Uysal and Yesim Ak.