Optimization of spacing and fertilizer requirement for prerelease cotton varieties under irrigated condition

Due to increasing scarcity of fresh water, use of unconventional water source (e.g., wastewater) in irrigation has now become important. However, inclusive information on the effects of wastewater on crop production and soil health is necessary for such intervention. This study was designed to evaluate these effects by demonstrating the contribution of municipal wastewater (hereafter called wastewater) on yield and nutrient requirement of wheat (<i>Triticum aestivum</i> L.) cv Shatabdi. Five irrigation treatments - I1, I2, I3, I4 and I5 were tested in a Randomized Complete Block Design (RCBD) with three replications during November-March of 2007-2008, 2008-2009, 2009-2010 at the experimental field of the Bangladesh Agricultural University, Mymensingh. The treatments I2-I5 consisted of blended wastewater and I1 of fresh water (control). The ratio of wastewater to total irrigation water was 0.25, 0.50, 0.75 and 1.0 in I2, I3, I4 and I5, respectively. Wheat was cultivated with three irrigations and recommended doses of fertilizer in three consecutive years. Wastewater contained nitrogen (N), phosphorus (P) and potassium (K) @ 17.5, 3.7 and 10.3 mg/L, respectively, and irrigation by raw wastewater (I5) contributed 19.1, 15.1 and 21.7% of the recommended N, P and K, respectively. Biomass yield increased with increasing fraction of wastewater in irrigation. Grain yield increased for the wastewater fraction of 0.50 - 0.75 in irrigation but decreased when irrigation was applied by raw wastewater. Excess fertilizer (under I5) boosted up growth of wheat, but did not contribute to the grain yield. Number of grains per spike; and grain, straw and biological yields significantly (p = 0.05) increased due to the contribution of wastewater. Wastewater significantly improved grain and biomass production, with the largest value obtained in I4 (4.61 t/ha grain yield and 11.36 t/ha biomass yield). Raw wastewater in combination with recommended fertilizer doses caused over-fertilization that contributed only in biomass production but not in grain production of wheat and irrigation by wastewater substantially reduced fertilizer requirement of wheat.The Agriculturists 2016; 14(1) 01-14

Download Full-text

Fertilizer Requirement for Densely Planted Okra (Abelmoschus esculentus L.)

Tropical Agricultural Research ◽

10.4038/tar.v21i3.3301 ◽

2011 ◽

Vol 21 (3) ◽

pp. 275 ◽

Cited By ~ 1

Author(s):

AMKCK Abeykoon ◽

RM Fonseka ◽

S Paththinige ◽

KWLK Weerasinghe

Keyword(s):

Abelmoschus Esculentus ◽

Fertilizer Requirement

Download Full-text

APPROXIMATING THE POTASSIUM FERTILIZATION REQUIREMENT OF COTTON ON SOME REPRESENTATIVE SEMIARID TROPICAL SAVANNAH SOILS OF NIGERIA

Canadian Journal of Soil Science ◽

10.4141/cjss81-057 ◽

1981 ◽

Vol 61 (3) ◽

pp. 507-516

Author(s):

GANYIR LOMBIN

Keyword(s):

Cotton Yield ◽

Seed Cotton ◽

Potassium Fertilizer ◽

Quadratic Polynomials ◽

Independent Variables ◽

Regression Procedure ◽

Fertilizer Requirement ◽

Relevant Variables ◽

Multiple Determination

Potassium fertilizer requirement of rain-fed cotton was evaluated in a 3-yr field study conducted at three locations using four rates (0, 25, 50 and 75 kg∙ha−1)of K. Significant response was not obtained above 25 kg∙ha−1 applied K. Quadratic polynomials, using leaf K, exch. K, exch. (Ca + Mg)/K and applied K as independent variables, were fitted to the seed-cotton yield. As a single parameter, leaf K emerged the best predictor of yield with a coefficient of multiple determination (R2) of 86% and a corresponding regression equation of: Y (yield) = 3099.2 + 6031.6%K − 1643.3 (%K)2, followed by fertilizer K with a coefficient of multiple determination of 81% and a yield equation of: Y = 1302.3 + 53.96 app. K − 0.54 (app. K)2. Soil exch. K and (Ca + Mg)/K ratio were slightly less efficient in predicting yield giving R2 values of 0.62 and 0.76, respectively, when both the linear and quadratic terms were entered into their respective yield equations. When all the 12 possible entries (linear, and second-order terms and their square root transformations) were fed into the computer and regressed over cotton yield using a step-wise regression procedure, only two variables, leaf %K and (Ca + Mg)/K, significantly fitted the yield equation giving a predictive value of 87%. But the improvement in the precision of yield predictability as measured by the R2 value was only marginal and would not justify recommending the equation considering the extra laboratory work that will be needed to obtain the relevant variables. Critical values of 1.84% in index leaf, 0.19 meq/100 g exch. K and 50 kg∙ha−1 applied K were approximated for a maximum predicted yield range of 2440–2700 kg∙ha−1.

Download Full-text

Yield responsiveness and response curvature as essential criteria for the evaluation and calibration of soil phosphate tests for wheat

Soil Research ◽

10.1071/sr9850167 ◽

1985 ◽

Vol 23 (2) ◽

pp. 167 ◽

Cited By ~ 24

Author(s):

ICR Holford ◽

JM Morgan ◽

J Bradley ◽

BR Cullis

Keyword(s):

Field Experiments ◽

Yield Response ◽

Soil Test ◽

Wheat Field ◽

Fertilizer Effectiveness ◽

Soil Phosphate ◽

Fertilizer Requirement ◽

Dry Conditions ◽

Using Data ◽

Actual Response

In a study using data from 57 wheat field experiments on the central-western slopes of New South Wales, eight soil phosphate tests (Bray,, Bray,, alkaline fluoride, Mehlich, Truog, lactate, Olsen and Colwell) were evaluated and calibrated in terms of responsiveness (�) and response curvature (C) parameters derived from the Mitscherlich equation. The results showed that, regardless of how well correlated a soil test is with yield responsiveness, it cannot give a satisfactory estimate of fertilizer requirement unless yield response curvature is also taken into account. The tendency of soil test values, especially of the Colwell test, to be negatively related to response curvature, and hence inversely related to fertilizer effectiveness, compounded the problem of directly relating soil test values to fertilizer requirement. The best test (lactate) accounted for only 28% of the variance in fertilizer requirement, compared with 50% of the variance in responsiveness, and the worst test (Colwell) was completely unrelated to fertilizer requirements. When fertilizer requirement was estimated from the lactate test value and the actual response curvature for each experiment, 68% of the variance (from the actual fertilizer requirement) was accounted for. Thirteen experiments were subject to drier conditions than the others, and these were less responsive and had lower fertilizer requirements relative to soil test values. In relation to yield responsiveness, the Colwell test was most sensitive (P < 0.001) to dry conditions, while the two best tests (lactate and Bray,) were the least sensitive (P > 0.05). The results demonstrated the superiority of acidic anionic extractants over alkaline bicarbonate extractants on moderately acid to alkaline wheat-growing soils.

Download Full-text

Determination of Site Specific Fertilizer Requirement of Sugarcane and Intercrops in Sugarcane-based Cropping Systems

Pakistan Journal of Biological Sciences ◽

10.3923/pjbs.2002.165.168 ◽

2002 ◽

Vol 5 (2) ◽

pp. 165-168 ◽

Cited By ~ 1

Author(s):

S.M. Bokhtiar ◽

M.L. Kabir ◽

M.J. Alam ◽

M. M. Alam ◽

M.H. Rahman

Keyword(s):

Cropping Systems ◽

Site Specific ◽

Fertilizer Requirement

Download Full-text

RATE OF RELEASE OF NON-EXCHANGEABLE POTASSIUM BY ONTARIO SOILS IN RELATION TO NATURAL SOIL CHARACTERISTICS AND MANAGEMENT PRACTICES

Canadian Journal of Soil Science ◽

10.4141/cjss58-006 ◽

1958 ◽

Vol 38 (1) ◽

pp. 36-43 ◽

Cited By ~ 18

Author(s):

H. B. McEwen ◽

B. C. Matthews

Keyword(s):

Management Practices ◽

Clay Content ◽

Soil Characteristics ◽

Soil Testing ◽

Natural Soil ◽

Exchangeable Potassium ◽

Fertilizer Requirement ◽

Predominant Factor ◽

Intensive Cropping ◽

Power Measurements

The rate of release of non-exchangeable potassium, i.e. potassium-supplying power, of 41 Ontario soils was measured by a continuous percolation procedure. It was found that clay content of the soil was the predominant factor affecting potassium-supplying power (r = 0.978). Potassium fertilization or intensive cropping of the soil caused no change in the potassium-supplying power of the soil. As potassium-supplying power was found to be a constant characteristic of soil and not a function of previous management, potassium-supplying power measurements should not be necessary in routine soil testing. Knowledge of potassium-supplying power can be deduced from particle size distribution. Because soils of different texture have different potassium-supplying power, the interpretation of measured exchangeable potassium in terms of fertilizer requirement will be different for soils of different textural class.

Download Full-text