Growth and Yield of Wheat under Irrigation by Sugar Mill’s Wastewater

2014 ◽  
Vol 24 (1-2) ◽  
pp. 211-218
Author(s):  
PK Kundu ◽  
TK Acharjee ◽  
MA Mojid
Keyword(s):  
Grain Yield ◽  
Fertilizer Application ◽  
Wastewater Irrigation ◽  
Growth And Yield ◽  
Sugar Mill ◽  
Yield Data ◽  
Full Dose ◽  
Sugar Mills ◽  
Irrigation Treatments ◽  
Mixed Water

The possibility of using sugar mill’s wastewater/effluent in irrigation was evaluated by investigating the effects of wastewater on growth and yield of wheat (Triticum aestivum cv. Prodip). The experiment was conducted at North Bengal Sugar Mill site in Natore during December 2011 to March 2012. Three irrigation treatments (I1: irrigation with fresh/tubewell water, I2: irrigation with a mixture of fresh and wastewater at 1:1 ratio and I3: irrigation with wastewater) under a main factor and three fertilizer treatments (F0: no application of fertilizer, F1: half dose fertilizer and F2: full dose fertilizer) under a sub factor were evaluated. The experiment was laid out in a split-plot design with three replications of the treatments. Wheat was grown with three irrigations totaling 14 cm applied at 4, 26 and 43 days after sowing (DAS). Important growth and yield data of the crop were recorded. The highest grain yield of 1.829 t/ha was obtained under mixed water irrigation and the lowest grain yield of 1.469 t/ha was obtained under wastewater irrigation. The three irrigation treatments, however, provided statistically similar (p = 0.05) grain yield. For the interaction between irrigation and fertilizers, mixed water irrigation and full dose fertilizer application (I2F2) provided significantly higher grain yield (2.757 t/ha) than all other treatment combinations. The second highest yield, produced under freshwater irrigation and full dose fertilizer (I1F2), was statistically similar to the yield under wastewater irrigation and full dose fertilizer (I3F2). Results of this experiment thus exposed good prospects of irrigating wheat by sugar mills’ wastewater.DOI: http://dx.doi.org/10.3329/pa.v24i1-2.19174 Progress. Agric. 24(1&2): 211 - 218, 2013

scholarly journals Effects of Dairy Farm’s Wastewater Irrigation on Wheat Production and Soil Health

2016 ◽  
Vol 8 (2) ◽  
pp. 157-162 ◽  
Author(s):  
MT Islam ◽  
AKM Adham ◽  
D Islam
Keyword(s):  
Grain Yield ◽  
Soil Health ◽  
Dairy Farm ◽  
Quality Parameters ◽  
Wastewater Irrigation ◽  
Growth And Yield ◽  
Yield Data ◽  
Wheat Production ◽  
Wheat Field ◽  
Raw Wastewater

An experiment was conducted at the experimental field of the Bangladesh Agricultural University (BAU) to investigate the effects of dairy farm’s wastewater irrigation on wheat (Triticum aestivum cv. BARI "Shatabdi") production and soil health during 30 November 2013 through 22 March 2014. Three irrigation and three fertilizer treatments were involved in the experiment. The experiment was laid down in a split-plot design with three replications of the treatments. Various growth and yield data of the crop were recorded. For the effect of irrigation water quality, the highest and the lowest grain yield (2.88 t ha-1 and 2.71 t ha-1) were obtained under I3 and I2, respectively. For the effect of fertilizer, the highest and the lowest grain yield (4.42 t ha-1and 1.59 t ha-1) were obtained under F2 and F0, respectively. The interaction effect between irrigation and fertilizer exerted a significant impact on the grain yield of wheat. Irrigation by wastewater did not significantly alter the quality parameters of the irrigated soils. The raw wastewater supplied more nutrients to the soil of the wheat field. Therefore, it may be used as a source of irrigation and fertilizer for wheat production without any hazard of the soil health.J. Environ. Sci. & Natural Resources, 8(2): 157-162 2015

scholarly journals Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

2020 ◽  
Vol 13 (1) ◽  
pp. 165
Author(s):  
Hillary M. O. Otieno ◽  
George N. Chemining’wa ◽  
Shamie Zingore
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nutrient Management ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
Yield Prediction ◽  
Maize Productivity ◽  
Dry Biomass ◽  
The Right

To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings—providing a tool for tracking and monitoring nutrient management effects—not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

scholarly journals Effect of integrated nutrient management on the growth and yield of boro rice (Oryza sativa L.) cultivars

2020 ◽  
Vol 5 (4) ◽  
pp. 476-481
Author(s):  
Swarna Chakraborty ◽  
Afrina Rahman ◽  
Md. Abdus Salam
Keyword(s):  
Grain Yield ◽  
Nutrient Management ◽  
Rice Variety ◽  
Poultry Manure ◽  
Growth And Yield ◽  
Integrated Nutrient Management ◽  
Inorganic Fertilizers ◽  
Full Dose ◽  
Prilled Urea ◽  
Boro Rice

For sustainable boro rice production in Bangladesh, a balanced fertilization to improve the current soil condition caused by mono-application of inorganic fertilizers and appropriate rice variety selection are very crucial. Therefore, an experiment was conducted at the Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh, during the boro season of December 2016 to May 2017 to evaluate the effect of integrated nutrient management on two boro rice cultivars. The varieties were BRRI dhan28 and BRRI dhan29 and eight kinds of nutrient management viz., control (no fertilizers), recommended dose of inorganic fertilizers (120-60-40 N, P2O5, K2O kg ha-1 + gypsum 60 kg ha-1 and ZnSO4 @ 10 kg ha-1), full dose of poultry manure @ 5 t ha-1, cowdung @ 10 t ha-1, poultry manure @ 2.5 t ha-1 + 50% prilled urea and full dose of other inorganic fertilizers, cowdung @ 5 t ha-1 + 50% prilled urea and full dose of inorganic fertilizers, poultry manure @ 2.5 t ha-1 + 75% prilled urea and full dose of inorganic fertilizers, and cowdung @ 5 t ha-1 + 75% prilled urea and full dose of other inorganic fertilizers. The experiment was laid out in a split plot design with three replications. Result showed that, yield and yield components of boro rice were significantly influenced by variety, nutrient management and interaction of variety and nutrient management. In respect of grain yield, BRRI dhan29 produced the maximum yield (5.46 t ha-1). BRRI dhan28 showed poor performance with all characters and gave the minimum yield (4.07 t ha-1). In case of nutrient managements, the highest yield and yield component were obtained from poultry manure @ 2.5 t ha-1 + 50% prilled urea and full dose of inorganic fertilizers and produced the maximum grain yield (5.70 t ha-1). In the interaction of variety and integrated nutrient management, the highest grain yield (6.83 t ha-1) and straw yield (7.61 t ha-1) was obtained from poultry manure @ 2.5 t ha-1 + 50% prilled urea and full dose of inorganic fertilizers with BRRI dhan29 variety. So, BRRI dhan29 with 2.5 t ha-1 poultry manure + 50% prilled urea and full dose of the recommended inorganic fertilizers might be a promising practice for boro rice cultivation.

scholarly journals Enhancing rice yield in acidic soil through liming and fertilizer management

2018 ◽  
Vol 16 (3) ◽  
pp. 357-365
Author(s):  
Syeda Ariana Ferdous ◽  
Mohammad Noor Hossain Miah ◽  
Mozammel Hoque ◽  
Sazzad Hossain ◽  
Ahmed Khairul Hasan
Keyword(s):  
Grain Yield ◽  
Crop Production ◽  
Growth Parameters ◽  
Block Design ◽  
Fertilizer Application ◽  
Chemical Fertilizer ◽  
Acidic Soil ◽  
Growth And Yield ◽  
Treatment Combination ◽  
Four Levels

The effect of lime and fertilizer application, as the management of soil acidity, on the growth and yield of rice cv. BRRI dhan50 was investigated during Aman rice season at the Agronomy Field Laboratory of Sylhet Agricultural University, Bangladesh. The experiment was consisted of two factors namely lime and fertilizer. There were four levels of lime (0, 0.50, 1.00, and 1.50 t ha–1 of CaCO3.MgCO3) and three levels of fertilizers (control, FYM @ 10 t ha–1, and chemical fertilizer @ 100-30-42-4-3-0.4 kg ha–1 of N-P-K-Ca-S-Zn). The experiment was laid out in a randomized complete block design with three replications where the unit plot size was 4.0 m x 2.5 m. Growth parameters, yield components and yield of BRRIdhan 50 rice increased with increasing lime rate in association of fertilizer in acidic soil. The highest grain yield (2.90 t ha–1) was recorded from the application of 1.50 t ha–1 lime and the lowest (2.06 t ha–1) was from control (0t ha–1), irrespective of fertilizer. On the other hand, the best effect of fertilizers on grain yield (3.08 t ha–1) was found with the application of FYM @ 10 t ha–1 and the lowest yield (1.59 t ha–1) was in control. The treatment combination of lime 1.50 t ha–1 and FYM (@ 10 t ha–1 produced the highest grain yield (3.60 t ha–1), which was followed by treatment combination of lime 1.50 t ha–1 and chemical fertilizer @ 100-30-42-4-3-0.4 kg ha–1 of N-P-K-Ca-S-Zn (3.28 t ha–1). Additionally, application of lime and FYM improved the soil fertility and properties of acidic soil for crop production by increasing the pH, organic matter and availability of some essential nutrients. From the study, it was indicated that both FYM and lime could affect to enhance the grain yield of rice in acidic soil. J. Bangladesh Agril. Univ. 16(3): 357–365, December 2018

Wheat yield as influenced by urea applied with nitrification inhibitor and gibberellic acid

2020 ◽  
Author(s):  
Rayehe Mirkhani ◽  
Mehdi Shorafa ◽  
Mohammad Zaman
Keyword(s):  
Grain Yield ◽  
Crop Yield ◽  
Stem Elongation ◽  
Block Design ◽  
Wheat Yield ◽  
Nitrification Inhibitor ◽  
Growth And Yield ◽  
Control Treatment ◽  
Yield Data ◽  
Economic Implications

<p>Among the essential plant nutrients, nitrogen (N) is the most needed. Farmer apply N fertilizer, predominantly urea to meet crop N demand. However, a greater proportion of the applied urea-N is not being used by plants and lost to the atmosphere as ammonia or greenhouse gases. Therefore, it is necessary to enhance N use efficiency (NUE) of applied urea by minimizing such losses, which has environmental and economic implications. Nitrification inhibitor, such as nitrapyrin (NP), has the most potential to minimise N losses and enhance crop yield. Similarly, plant hormones, such as GA3, has the potential to reduce abiotic stress and improve plant growth and yield.   <br>A field experiment was established on an arable site at University of Tehran, Karaj to determine the effect of urea applied with Nitrapyrin and GA3 on wheat yield in 2018-2019. Karaj has a Mediterranean climate with annual precipitation of 265 mm. A randomized complete block design in five replications was used in this study. Treatments were: T1 (control treatment - without urea), T2 (farmers practice - 138 kg N/ha), and T3 (best practice - 138 kg N/ha+NP+GA3). Urea was applied in three split applications (46 kg N/ha) at growth stage (GS 21) or tillering, (GS 32) or stem elongation, and (GS 40) or booting. GA3 in T3 treatment, was applied only at stem elongation stage. <br>The crop yield data showed that, urea applied with NP and GA3 had a significant (p ≤ 0.01) effect on grain yield, biological yield, number of grains, 1000-grain weight and % Harvest Index (%HI) compared to other treatments. Urea applied with NP and GA3 increased grain yield (10.30 t ha-1) by 13.9% and 46.1% compared to farmer practices (9.04 t ha-1) and control treatment (7.05 t ha-1). These results suggest that co-application of urea with NP and GA3 has the potential to enhance wheat yield in semi-arid area of Iran.</p>

scholarly journals Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 241 ◽  
Author(s):  
Quanxiao Fang ◽  
L. Ma ◽  
R. D. Harmel ◽  
Q. Yu ◽  
M. W. Sima ◽  
...  
Keyword(s):  
Grain Yield ◽  
Model Prediction ◽  
Model Calibration ◽  
Goodness Of Fit ◽  
Root Zone ◽  
Latin Hypercube Sampling ◽  
Crop Modeling ◽  
Quality Model ◽  
Yield Data ◽  
Irrigation Treatments

An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals.

Optimizing sowing density-based management decisions with different nitrogen rates on smallholder maize farms in Northern Nigeria

2020 ◽  
Vol 56 (6) ◽  
pp. 866-883
Author(s):  
Adnan Aminu Adnan ◽  
Jan Diels ◽  
Jibrin Mohammed Jibrin ◽  
Alpha Yaya Kamara ◽  
Abdulwahab Saliu Shaibu ◽  
...  
Keyword(s):  
Nutrient Management ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
Planting Density ◽  
Economic Returns ◽  
Yield Data ◽  
N Fertilizers ◽  
Scenario Analyses ◽  
Maize Varieties

AbstractIn this study, the CERES-Maize model was calibrated and evaluated using data from 60 farmers’ fields across Sudan (SS) and Northern Guinea (NGS) Savannas of Nigeria in 2016 and 2017 rainy seasons. The trials consisted of 10 maize varieties sown at three different sowing densities (2.6, 5.3, and 6.6 plants m−2) across farmers’ field with contrasting agronomic and nutrient management histories. Model predictions in both years and locations were close to observed data for both calibration and evaluation exercises as evidenced by low normalized root mean square error (RMSE) (≤15%), high modified d-index (> 0.6), and high model efficiency (>0.45) values for the phenology, growth, and yield data across all varieties and agro-ecologies. In both years and locations and for both calibration and evaluation exercises, very good agreements were found between observed and model-simulated grain yields, number of days to physiological maturity, above-ground biomass, and harvest index. Two separate scenario analyses were conducted using the long-term (26 years) weather records for Bunkure (representing the SS) and Zaria (representing the NGS). The early and extra-early varieties were used in the SS while the intermediate and late varieties were used in the NGS. The result of the scenario analyses showed that early and extra-early varieties grown in the SS responds to increased sowing density up to 8.8 plants m−2 when the recommended rate of N fertilizers (90 kg N ha−1) was applied. In the NGS, yield responses were observed up to a density of 6.6 plants m−2 with the application of 120 kg N ha−1 for the intermediate and late varieties. The highest mean monetary returns to land (US$1336.1 ha−1) were simulated for scenarios with 8.8 plants m−2 and 90 kg N ha−1, while the highest return to labor (US$957.7 ha−1) was simulated for scenarios with 6.6 plants m−2 and 90 Kg N ha−1 in the SS. In the NGS, monetary return per hectare was highest with a planting density of 6.6 plants m−2 with the application of 120 kg N, while the return to labor was highest for sowing density of 5.3 plants m−2 at the same N fertilizer application rates. The results of the long-term simulations predicted increases in yield and economic returns to land and labor by increasing sowing densities in the maize belts of Nigeria without applying N fertilizers above the recommended rates.

scholarly journals Strategy of nitrogen fertilizer application to increase growth and yield of rice in ratoon system at tidal swampland

2020 ◽  
pp. 1004-1010
Author(s):  
Gribaldi Gribaldi ◽  
Nurlaili Nurlaili ◽  
Firnawati Sakalena ◽  
Nurmala Dewi ◽  
Ardi Asroh
Keyword(s):  
Grain Yield ◽  
Nitrogen Fertilizer ◽  
Nitrogen Fertilization ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
Good Effect ◽  
Rice Varieties ◽  
Main Crop ◽  
Main Plot ◽  
Plot Design

This study aims to determine the effect of regulating the provision of nitrogen fertilizer on several rice varieties on the growth and yield of rice in ratoon system at the Tidal swampland. A split plot design was employed in this experiment. The main plot was nitrogen fertilizer application (N) consisting of N1, N2, N3, and N4. The subplot is rice varieties (V) consisting of Inpari 30 (V1), Inpara 3 (V2), Inpari 33 (V3), Inpari 43 (V4) and Hipa 5 Ceva (V5). The results showed that ½ dose nitrogen fertilization given at planting + ½ dose at primordia had a good effect on the growth and yield of the main crop, whereas 1/3 dose nitrogen fertilization given at planting + 1/3 dose at primordia + 1/3 dose at harvest tends to have a good effect on ratoon yield. Variety Hipa 5 Ceva produced highest yield of unhusked rice (i.e. 4.9 tons ha-1 for the main crop and 2.71 tons ha-1 for ratoon) at (N3): 135 kg N ha-1 fertilizer, when ½ dose was given at planting + ½ dose at primordia. The variety Hipa 5 Ceva with various nitrogen fertilization strategies provided the highest total grain yield in the ratoon system at tidal swampland.

scholarly journals Sensitivity Analysis of CERES-Wheat Model under Hisar Conditions

2021 ◽  
Vol 10 (9) ◽  
pp. 368-374
Author(s):  
M. K. Nayak Raj Singh ◽  
Anil Kumar Surender Singh ◽  
Deepak Patil
Keyword(s):  
Grain Yield ◽  
Growth And Yield ◽  
Rabi Season ◽  
Yield Data ◽  
Sunshine Hours ◽  
Dssat Model ◽  
Main Plot ◽  
Genetic Coefficient ◽  
Dough Stage ◽  
Agricultural Meteorology

The field experiment was conducted at Research Farm, Dept of Agricultural Meteorology, CCS HAU, Hisar (Lat.: 290 10’ N, Log.: 75036’ E & 215.2 m above msl), Haryana, India during the rabi season of 2014-15 and 2015-16. Experimental designed laid out with strip plot, as main plot treatment: four growing environments i.e. D1: 5th Nov., D2: 20th Nov., D3: 5th Dec. and D4: 20th Dec. and sub-plot treatment: four irrigation regimes, it applied at different phenophases (I1-CRI, I2- CRI and heading, I3- CRI+ jointing and milking, I4- CRI+ jointing + anthesis and dough stage). Crop growth and yield data of 2014-15 were used for calibration of DSSAT model and cultivar coefficients for WH1105 based on the observed crop characteristics. Genetic coefficient parameters are in the ranges obtained by the few other studies conducted on wheat with the exception of parameters G1, G2, and G3, related to grain growth. Sensitivity of simulated grain yield to down scaled sunshine hours, solar radiation -0.5 to -2.5 hours and -1°C to -5 MJ-2 day-1 showed a gradual decrease in grain yield, respectively.

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