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Corn yield in sandy soil fertilized with poultry litter

Corn yield in sandy soil fertilized with poultry litter

These results corroborate to other studies in which increasing doses of poultry litter provided significant increase in corn yield (Bayer et al., 1999; Konzen, 2003; Novakowiski et al., 2013). Likewise, there was an increasing linear behavior of corn yield in relation to the increase of poultry litter levels in a Ultisol (Gomes et al., 2005). The increase in yield was a result of the increase in organic matter content, pH and soil nutrients with the addition of poultry litter. It was evidenced by the levels in the soil after corn harvest, which were higher than the levels verified before the experiment (Figs. 2 and 3).

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Corn Yield Response to some Irrigation Methods and Fertilization with Macro and Micronutrients

Corn Yield Response to some Irrigation Methods and Fertilization with Macro and Micronutrients

In the present study grain yield and its components influenced by the irrigation method and the combination between macro and micro fertilization. Significantly, lower yield and magnitudes of yield components were obtained when lower amount of water was supplied during the growth. The results showed that (CFI) followed by (AFI) increased grain yield and yield contributing characters, in respect to (FFI). However, the highest (IWE) was obtained with (FFI) and (AFI) methods which save about 24% and 13% of irrigation water in the first season and about 15% and 26% in the second season, respectively. Under studied irrigation methods, combination of macro and micro nutrients (F4) treatments significantly increased grain yield and its components compared with macronutrient only (F1) treatment. It is recommended that alternate furrow irrigation can be used as a simple and efficient method for corn production as it enables the production of as much corn yield as those offered by all furrow method. Also, optimum fertilization must be added and more attention should be paid to micronutrients nutrition for corn plants so that plant growth and yields are not limited by nutrient deficiencies occurred as a result of small amount of irrigation water.

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Climate Effects on Corn Yield in Kermanshah Provinceby

Climate Effects on Corn Yield in Kermanshah Provinceby

particularly sensitive to rainfall shortly before anthesis and the maximum temperature during anthesis. A study by Coelho and Dale (1980), using 1972–74 data from West Lafayette, Indiana, found that corn growth and yield were favored by warmer temperatures from emergence to silking, a result somewhat different from that of Smith. Although depicting some climate influences on corn yield, these studies are primarily ‘‘case studies,’’ and their results are difficult to use to gain a comprehensive understanding of climate effects on corn yield because of the short-term records for different years and localities. Additional studies using long-term data of climate and corn yield from a location or a region are needed to IJABBR- 2014- eISSN: 2322-4827

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Effects of Cover crop residue management on corn yield and weed control

Effects of Cover crop residue management on corn yield and weed control

The results of the effect of cover crops and their management showed that although cover crop treatments were in the same group considering one hundred seed weight, seed weight treated with the mixture of clover + wheat were less in both managed (table5). Given that the test treatment crimson clover + mixture produced less biomass due to clover slow growth plants and nutrients (especially nitrogen) were absorbed less from the soil during its growth. Therefore the remaining nitrogen and more moisture had stored in the soil which could be used by the next season ultimately increasing the yield of maize (Table 5).

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Corn Yield Response to Reduced Water Use at Different Growth Stages

Corn Yield Response to Reduced Water Use at Different Growth Stages

Groundwater levels have declined across much of the United States including the Mississippi Delta [3] [4]. Due to the increased cost of energy and overdraft of the Mississippi Delta Shallow Alluvial Aquifer, there is a need to use irrigation water efficiently in the region. In order to develop an efficient water use strategy in crop irriga- tion, we need to know how much water can be reduced without decreasing yield. This depends, among other things, on the growth stage of the plant, its rate of water use and plant available water (which depends on the soil type) involving evapotranspiration demand. Corn requires different amounts of water at different growth stages. It requires less water at the early and late growth stages, while the peak water requirement occurs during the pe- riod two weeks before and after silking [5]-[7]. In the Mississippi Delta, there is usually adequate precipitation during the vegetative growth stages of corn plants (April-May), but during the reproductive and the grain fill stages (June-August) there is frequent drought and irrigation is required to avoid yield loss. Therefore, the need for efficient use of irrigation water in corn in the Mississippi Delta arises mainly during the reproductive and the grain fill stages of corn.

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Water Requirements for Corn Yields in the Northern Regions of Cameroon Using AquaCrop Model

Water Requirements for Corn Yields in the Northern Regions of Cameroon Using AquaCrop Model

Having reached the end of our work or it was question of estimating using the model AquaCrop of the quantities of water necessary for a good yield of corn in the regions of north Cameroon and therefore the objective is part of the will of help producers to use climate information in planning and decision making to maximize agricultural yields and ensure food security. Best corn yields were obtained in the rainy season with irrigation. The amounts of irrigation water are on average around 72.15 mm in the rainy season and on average around 427.03 mm in the dry season. Thus, these results indicate that it is possible to cultivate maize plants in the regions of Maroua, Garoua and Kaélé with a high yield; and certainly, in far north Cameroon generally and not just in July as during the rainy season. In terms of perspective, it will be useful to analyses, using the projection model, the impact of the climate change on the corn yield over this region.

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The perspectives for genetically modified cellulosic biofuels in the Central European conditions

The perspectives for genetically modified cellulosic biofuels in the Central European conditions

age share of the corn sown area dedicated to the GM corn in 2011 is 2.6%. By the FE model, we estimate that if 18.4 more percentage points of the corn sown area were dedicated to the GM corn, an increase of 10.6% would be achieved in the crop yield. The 95% confidence interval for our estimate is (0.2, 21.2). Such broad confidence interval can be explained by the heterogeneity of the regions. Nevertheless, the important information contained in this prediction is the positive sign of the effect. The average yield in 2011 was 42 t/ha. Using the 10.6% as the best predic- tion, the adoption of the GM corn to 21% of the corn sown area would increase the yield to 46.5 t/ha. Such yield overcomes the maximum yield of our data set by more than 1 t/ha. When interpreting these results, we should keep in mind that our linear regression provides us with coefficients for marginal changes. Therefore, if we consider more sizeable changes in the values of variables, these marginal changes im- plied by the regression coefficients obviously do not have to hold exactly true. Similarly, the linearity of our model does not imply that the true underlying share of the GM corn in the overall corn yield has to be linear too. The linearity of our model is just a modelling simplification.

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Phosphate Biofertilizer, Row Spacing and Plant Density Effects on Corn (Zea mays L ) Yield and Weed Growth

Phosphate Biofertilizer, Row Spacing and Plant Density Effects on Corn (Zea mays L ) Yield and Weed Growth

A field study was conducted at the Agricultural Research Farm of Razi University, Kermanshah, Iran to investigate the effects of phosphate biofertilizer, row spacing and plant density on corn yield and weed growth. The experiment was a factorial with three factors arranged in a randomized complete block design with three replications. The first factor was phosphate biofertilizer (inoculation and non-inoculation), the second was row spacing (conventional (75 cm) and re- duced (50 cm)) and the third was plant density (66,666 plants·ha –1 (conventional plant density) 83,333 and 99,999 plants·ha –1 (1.25 and 1.5 times the conventional plant density, respectively)). Results indicated that corn yield and weed growth were significantly influenced by row spacing and plant density. So that, corn yield improved and weed biomass diminished in response to increasing plant density and decreasing row spacing. However, phosphate biofertilizer had no significant effect on corn yield, whereas, weed biomass was notably increased when phosphate biofertilizer was applied. Overall, this study revealed that both yield and weed control in corn field can be improved by alteration of the planting arrangement.

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Corn Pollination and Yield as Influenced by Weed Density and Row Pattern.

Corn Pollination and Yield as Influenced by Weed Density and Row Pattern.

cultivar expressing resistance to both glufosinate and glyphosate. Corn was 25 to 36 cm in height when herbicides were applied. Visible estimates of percent common ragweed (Ambrosia artemisiifolia L.) control were determined 3 weeks after POST application. Density of common ragweed was also determined 3 and 6 weeks after POST herbicides were applied. In addition to corn yield, yield components and number of days from planting to silk emergence were determined. Data for common ragweed density and visible control, corn grain yield, corn height, corn ear type (complete, incomplete, nubbin, and blunt), and days from planting to silk emergence were subjected to ANOVA appropriate for the factorial treatment structure and means were separated using Fisher’s Protected LSD test at p ≤ 0.05. Pearson Correlation Coefficients were determined for weed population and visible control vs. corn parameters at p ≤ 0.05.

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Biochar and manure effects on net nitrogen mineralization and greenhouse gas emissions from calcareous soil under corn

Biochar and manure effects on net nitrogen mineralization and greenhouse gas emissions from calcareous soil under corn

A number of studies across North America have employed the same biochar to better understand how biochar effects vary for different soils (Spokas et al., 2009; Husk and Major, 2011; Dumroese et al., 2011; Elmer and Pignatello, 2011; Lentz and Ippolito, 2012). The use of only one biochar avoids obfusca- tion caused by variation in biochar properties. The biochar was derived from hardwood waste biomass, CQuest (Dynamotive Energy Systems, West Lorne, Ontario, Canada) and had a neu- tral to slightly acid pH. A Minnesota study found that addition of 10% (w/w) CQuest biochar to an acidic silt loam soil (pH = 6.5) generally suppressed CO 2 , CH 4 , and N 2 O production rate during a 100-d incubation (Spokas et al., 2009). This re- sult suggested that the biochar stabilized soil organic C, which has implications for N and S availability since they are substan- tially derived from organic sources. In an earlier paper, Lentz and Ippolito (2012) reported the effect of CQuest biochar and dairy manure amendments, and their interaction on soil chemi- cal properties, crop nutrient uptake, and corn yields for the same plots employed in this current study. Lentz and Ippolito (2012) found that biochar decreased corn yield and N uptake in the second year after biochar was applied but had little influence on nutrient levels determined in annual soil samples. We hypoth- esized that biochar may have influenced corn yield and N uptake via effects on soil N cycling; if so, this would imply that biochar influences on N cycling are persistent and not necessarily the re- sult of transient processes such as the release of ethylene (Spokas

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Sample size for estimation of direct effects in path analysis of corn.

Sample size for estimation of direct effects in path analysis of corn.

Alvi et al. (2003) measured 10 plants per plot with two replicates to estimate the direct and indirect effects of explanatory variables on corn YIELD. Thus, based on 20 plants, in each hybrid or line, the direct effects of PH, EIH, EL, ED, NR, number of grains per row, and one- thousand-grain weight on YIELD were estimated. Langade et al. (2013) evaluated 10 plants per plot to measure the variables in ten corn lines, grown in a randomized block design with three replicates, and used the means of plots, that is, 30 observations (10 lines x 3 replicates), for estimating the direct and indirect effects in the path analysis. Jayakumar et al. (2007) evaluated five plants per treatment in three replicates to measure 14 traits used in the path analysis of corn lines and controls. Lopes et al. (2007) evaluated 15 plants per hybrid (five plants/plot x 3 replicates) and performed a general path analysis with n = 90 plants in an experiment conducted with six corn hybrids (15 plants per hybrid x six hybrids). However, the path analyses of each type of hybrid were performed based on only n = 30 plants (15 plants per hybrid x two hybrids).

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Genetic variability and correlation among yield and quality traits in  sweet corn

Genetic variability and correlation among yield and quality traits in sweet corn

correlated or independent. The present finding exhibits substantial opportunities to the breeders for improvement of these traits in maize cultivars and also suggests further exploration of a new source of elite breeding stocks containing a high level of these nutritionally important compounds. Finally, these findings may also help in bio-fortification of maize. High genotypic positive correlation was observed between days to tasselling-days to silking, days to tasseling- days to maturity and days to silking-days to maturity. In these traits the positive correlation indicates that selection for earliness can be done by using the basis of these traits simultaneously. Grain yield showed the positive correlation with plant height, cob placement height, cob length, cob width, grain rows cob -1 , and grains row -1 . So, the yield attributing traits can be used for selection programme and yield can be enhanced through increasing the yield component traits.

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Analysis of the Influence Factors of Grain Supply Demand Gap in China

Analysis of the Influence Factors of Grain Supply Demand Gap in China

DOI: 10.4236/as.2018.97062 905 Agricultural Sciences reach a balance. But supply and demand of major grain varieties can’t reach equilibrium between supply and demand. The demand of wheat in 2011 is greater than yield, but between 2012 and 2016, the supply is gradually increasing, and the gap between supply and demand is rapidly changing from negative to positive and gradually increasing. However, according to the recent import situ- ation of high-quality wheat, it can be seen that its import quantity is increasing year by year but the export quantity is not large, indicating that there is a large amount of wheat stock every year. The remaining production of corn is always large, and the gap between supply and demand in 2014 reached 60.29 million tons. As the country’s supply-side reform policy’ regulation of corn production, its yield and inventory has declined in 2015 and 2016. As far as soybean is con- cerned, its demand has been growing rapidly, but its yield can not keep up with the demand. The gap between supply and demand is increasing year by year due to its increase of yield.

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Comparison of nitrogen fertilization methods and rates for subsurface drip irrigated corn in the semi-arid Great Plains

Comparison of nitrogen fertilization methods and rates for subsurface drip irrigated corn in the semi-arid Great Plains

Main effects of N rate and application method were signif‐ icant (P < 0.05), and the subplot main effect of SPAD reading time as well as the SPAD reading time by application method interaction were also significant (table 6). The significant in‐ teraction is presented in figure 5. Chlorophyll meter SPAD readings in 2006 were greater for the ES application method at the V10 and V14 growth stages compared to the IS applica‐ tion method. However, at the R3 growth stage, the IS applica‐ tion method had a greater SPAD reading compared to the ES application method. These data indicate that, in 2006, the corn in the ES treatments had a greater N uptake early in the season (vegetative growth stages), but the corn in the IS treat‐ ment had a greater N uptake during the reproductive stages. Thus, the higher grain yield and total biomass for the IS treat‐ ment was possibly due to an adequate N supply during the lat‐ er growth stages compared to the ES treatment. The ES treatment likely had a decreased N supply due to N losses lat‐ er in the season. In 2006, the apparent reduced N uptake dur‐ ing the vegetative growth stages (to a point) was not as critical as reduced N availability during reproductive growth stages. A greater supply of N may have been needed earlier in the growing season under the IS treatment. In hindsight, supplying a portion of the total N around planting for the IS treatment likely would have offset the difference in plant N content during the vegetative growth stages.

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Performance of Baby Corn (Zea Mays L ) in Integration of Organic and Inorganic Nitrogen

Performance of Baby Corn (Zea Mays L ) in Integration of Organic and Inorganic Nitrogen

level of nitrogen fertilization might have played a significant role in reducing competition for photosynthates and nutrients with neighbor. The available photosynthates might have influence the number of flowers and their fertilization which ultimately ensure higher yield attributes. The positive correlation of grain yield and yield attributes to higher nitrogen fertilization corroborate findings of several previous researches (Chillar & Kumar [6]; Gosavi & Bhagat [13]; Golada et al. [11]). The increased fodder yield with higher N fertilization was also recorded by some researchers (Gosavi & Bhagat [13]; Gulabrao [15]). As the result revealed that most of the growth parameters such as plant height, number of leaf number plant -1 was higher

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THE STRUCTURE AND YIELD LEVEL OF SWEET CORN DEPENDING ON THE TYPE OF WINTER CATCH CROPS AND WEED CONTROL METHOD

THE STRUCTURE AND YIELD LEVEL OF SWEET CORN DEPENDING ON THE TYPE OF WINTER CATCH CROPS AND WEED CONTROL METHOD

According to Brzeski et al. [1993], the quan- tity of biomass incorporated into the soil is one of the main factors determining the secondary effect of catch crops on yields. However, the impact is not always positive in the year following incor- poration and sometimes it may take two or even three years for the effect to become visible. In the present study, an increase in the catch crop bio- mass quantity (especially non-leguminous plants) was followed by a decline in sweet corn ear and kernel yields, which was particularly noticeable in 2010 and 2011 when precipitation in the first part of the growing season was lower compared with 2009. In 2010, marketable ear yield of plants cultivated after rye and ear number after rye and winter turnip rape were significantly lower, com- pared with hairy vetch and white clover. The mar- ketable ear yield and ear number per 1 ha of corn following winter turnip rape were significantly lower compared with white clover. A large quan- tity of rye and winter turnip rape biomass incor- porated may be a barrier in making it more dif- ficult for water to infiltrate into deeper soil strata. This, coupled with lower precipitation, resulted in corn producing poorer yields.

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An agronomic assessment of greenhouse gas emissions from major cereal crops

An agronomic assessment of greenhouse gas emissions from major cereal crops

Values for annual, rather than seasonal, GHG emis- sions are needed for these cropping systems as fallow periods can be a large contributor to net GHG fluxes. Moreover, treatment effects like rate of N fertilizer input and its impact on residue yield and C : N ratios can last beyond the growing season. Kaiser et al. (1998) reported for a cereal study in Europe that approxi- mately 50% of annual emissions occurred during the winter months (October to February) due to freezing and thawing events during this period. Unfortunately, a meta-analysis of annual GHG emissions was not pos- sible for two reasons. First, the large majority of studies only provided GHG emission data for the growing sea- son. Second, in many parts of the world, especially in the tropics, multiple crops are grown annually on the same piece of land. For example, rice–wheat systems Table 5 Summary of the results of the meta-analysis on the

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Evaluation of Nitrogen and Phosphorus Fertilizer Placement With Strip Tillage for Irrigated Pacific Northwest Corn Production

Evaluation of Nitrogen and Phosphorus Fertilizer Placement With Strip Tillage for Irrigated Pacific Northwest Corn Production

Conventional tillage treatments consisted of chisel plow, tandem disk, fertilizer application (on broadcast treatments), and roller harrow in the spring. Strip tillage was conducted using a Strip Cat implement developed by Twin Diamond Industries LLC in Minden, NE. Corn (Pioneer 3523, GDD = 2530 in 2007; Pioneer 38H66, GDD = 2370 in 2009) was planted to the study locations at a seeding rate of 76,600 seed/ha on 24 May and 2 June in 2007 and 2009, respectively. The study locations were irrigated with furrow irrigation in 2007 and sprinkler irrigation in 2009.

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Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Landsat-8 images and ten MODIS images. First, the STVIFM was applied to generate high spatial resolution time series images by integrating Landsat-8 and MODIS images. Second, the two-step filtering approach was improved by using the daily 𝑓APAR fitted by the CSDM to detect corn and soybean phenology at subfield scale: (1) the crop growth model CSDM was adopted to simulate daily 𝑓APAR for a known corn sample site and a known soybean sample site based on the daily mean temperature and the seven 𝑓APAR values calculated from the original and generated remote sensing images. (2) Based on the simulated shape model of corn and soybean with known phenological dates, the phenological dates for the remaining pixels of the image were obtained by introducing another three parameters to the CSDM function and the spatial maps of the phenological dates for SOS, DOS and EOS over the study site were produced. The parameters in the SAFY model were determined through the literature, forcing the SOS, DOS and EOS information integrated with the daily mean temperature and calibrating using the 10 remotely sensed GLAI values. The results show that the improved two-step filtering approach, by the integration of the CSDM, has a good ability in simulating daily 𝑓APAR and detecting crop phenology at pixel scale. The accuracy of biomass estimation was improved by about 4% in RRMSE for corn and soybean by forcing the phenological information derived from remote sensing images into the SAFY model. The SAFY model is able to obtain the ELUE for each pixel though the calibration and can accurately reflect the spatial variation of crop biomass. In addition, good correlations were found between ELUE and the 𝑓APAR 𝑚𝑎𝑥 during the growing season for corn and soybean.

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The result of water-dispersible colloids in manure on the transfer of dissolved and colloidal phosphorus by the medium of a soil column

The result of water-dispersible colloids in manure on the transfer of dissolved and colloidal phosphorus by the medium of a soil column

The 10 entries were planted at two sites. These are: the Faculty of Agriculture (FAG) and the T&R farms of the University of Ilorin in a typical Southern Guinea Savanna ecology (Lat. 8°29’N and 8°30’N; Long. 4° 30’E and 4° 32’E) during the 1999 cropping season. FAG farm is on a higher elevation while the T&R farm is at lower elevation. FAG farm is also high in %N, organic matter (%) and mineral contents of calcium and potassium while the T&R farm is higher in clay content as well as magnesium and available phosphorus. The experiment was laid out as a randomized complete block design (RCBD) on each site and each plot consisted of four rows 5M long with inter and intra-row spacing of 0.75 and 0.5 M respectively. Plantings were carried out on 28 th and 30 th of July, 1999 respectively. The plots were over planted but later thinned to two stands per hill to give a plant population of approximately 53,333 plants/ha. Weed was controlled at the FAG farm by hand while a pre-emergence herbicide application supplemented by one hoe weeding was carried out at the T&R farm. Fertilizer application at both sites was in split- dosage at three and seven weeks after planting at the rate of 80 kg N, 60 kg P and 60 kg/ha respectively from compound NPK fertilizer (20-10-10). Data were collected on seedling emergence and days to 50% anthesis and silking, plant and ear heights (cm) and grain yield (kg/plot). Data from seedling emergence were used to compute emergence percentage (E %) while grain yield obtained in kg/plot was converted to tones/hectare (t/ha -1 ) assuming 85% shelling percentage after adjusting to 12% moisture at harvest.

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