Effects of Long Term Application of Inorganic and Organic Fertilizers on Soil Organic Carbon and Physical Properties in Maize–Wheat Rotation

Brar, Babbu Singh; Singh, Jagdeep; Singh, Gurbir; Kaur, Gurpreet

doi:10.3390/agronomy5020220

Cited by 199 publications

(90 citation statements)

References 56 publications

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“…Each unit increase in C/N ratio increased water-stable aggregates by 20.29%. A positive correlation of water-stable aggregates with the C was also reported by Brar et al (2015).…”

Section: Soil Physical Properties: Summer 2013supporting

confidence: 71%

“…Water-stable aggregates were highest in giant cane buffer compared with all other buffer treatments (Table 3), possibly due to the greater organic matter. The organic matter components, including humic molecules and polysaccharides, bind mineral particles into aggregates and thus increase aggregate stability (Brar et al, 2015). Orchardgrass had 6.86% greater water-stable aggregates than the bare ground treatment.…”

Section: Soil Physical Properties: Summer 2013mentioning

confidence: 99%

“…Organic matter components hold aggregates together, increasing their stability and soil porosity. Previous studies have reported that soil organic matter has a strong relationship with soil properties such as infiltration, productivity, aggregate stability, water-holding capacity, and erodibility, and thus it is an important indicator of soil quality (Bharati et al, 2002;Brar et al, 2015).…”

Section: Soil Physical Properties: Summer 2013mentioning

confidence: 99%

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Giant Cane Vegetative Buffer for Improving Soil and Surface Water Quality

et al. 2019

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Over the past four decades, riparian buffers have proven effective in retaining nutrients and sediment from agricultural runoff. Many grass species have been used with variable success in riparian buffers to improve the water quality of runoff. However, limited information is available on the effectiveness of giant cane [Arundinaria gigantea (Walt.) Muhl] in improving surface water quality compared with grass species such as Kentucky bluegrass (Poa pratensis L.) and orchardgrass (Dactylis glomerata L.). Therefore, the objective of our study was to determine the quality of runoff leaving vegetative buffer plots planted with giant cane, Kentucky bluegrass, and orchardgrass. Additionally, a bare-ground control and continuous corn (Zea mays L.) was also monitored for comparison of runoff with vegetative buffers. The giant cane treatment had significantly greater infiltration rates (38.18 mm h −1 , p < 0.05) than bare ground (1.61 mm h −1 ), corn (5.75 mm h −1 ), Kentucky bluegrass (12.30 mm h −1 ), and orchardgrass (4.21 mm h −1 ) treatments. Dissolved reactive P in runoff was ranked as follows: corn > giant cane = Kentucky bluegrass = orchardgrass > bare ground. The total P from the corn treatment (1.70 mg L −1 , p < 0.05) was significantly higher than for bare ground (1.22 mg L −1 ), giant cane (0.69 mg L −1 ), Kentucky bluegrass (0.86 mg L −1 ), and orchardgrass (0.54 mg L −1 ). Giant cane, Kentucky bluegrass, and orchardgrass significantly reduced the total P concentration more than bare ground and corn. Results from this study demonstrate the utility of giant cane as a vegetated buffer to reduce nutrient and sediment concentrations in agricultural runoff.

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“…Each unit increase in C/N ratio increased water-stable aggregates by 20.29%. A positive correlation of water-stable aggregates with the C was also reported by Brar et al (2015).…”

Section: Soil Physical Properties: Summer 2013supporting

confidence: 71%

Section: Soil Physical Properties: Summer 2013mentioning

confidence: 99%

Section: Soil Physical Properties: Summer 2013mentioning

confidence: 99%

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Giant Cane Vegetative Buffer for Improving Soil and Surface Water Quality

et al. 2019

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“…The reason for this irregularity may be caused by the previous season in 2010. There, corn was planted which can have positive effects on the organic humus content of the field, e.g., Singh Brar et al [30]. For site-years 1 and 4, the yield at N levels between 150 an 180 kg N·ha −1 had no increasing effect on the grain quantity or quality [4].…”

Section: Discussionmentioning

confidence: 99%

Utilisation of Ground and Airborne Optical Sensors for Nitrogen Level Identification and Yield Prediction in Wheat

et al. 2018

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Abstract:A healthy crop growth ensures a good biomass development for optimal yield amounts and qualities. This can only be achieved with sufficient knowledge about field conditions. In this study we investigated the performance of optical sensors in large field trails, to predict yield and biomass characteristics. This publication investigated how information fusion can support farming decisions. We present the results of four site-year studies with one fluorescence sensor and two spectrometers mounted on a ground sensor platform, and one spectrometer built into a fixed-wing unmanned aerial vehicle (UAV). The measurements have been carried out in three winter wheat fields (Triticum aestivum L.) with different Nitrogen (N) levels. The sensor raw data have been processed and converted to features (indices and ratios) that correlate with field information and biological parameters. The aerial spectrometer indices showed correlations with the ground truth data only for site-year 2. FERARI (Fluorescence Excitation Ratio Anthocyanin Relative Index) and SFR (Simple Fluorescence Ratio) from the Multiplex ® Research fluorometer (MP) in 2012 showed significant correlations with yield (Adj. r 2 ≤ 0.63), and the NDVI (Normalised Difference Vegetation Index) and OSAVI (Optimized Soil-Adjusted Vegetation Index) of the FieldSpec HandHeld sensor (FS) even higher correlations with an Adj. r 2 ≤ 0.67. Concerning the available N (N avail ), the REIP (Red-Edge Inflection Point) and CropSpec indices from the FS sensor had a high correlation (Adj. r 2 ≤ 0.86), while the MP ratio SFR was slightly lower (Adj. r 2 ≤ 0.67). Concerning the biomass weight, the REIP and SAVI indices had an Adj. r 2 ≤ 0.78, and the FERARI and SFR ratios an Adj. r 2 ≤ 0.85. The indices of the HandySpec Field ® spectrometer gave a lower significance level than the FS sensor, and lower correlations (Adj. r 2 ≤ 0.64) over all field measurements. The features of MP and FS sensor have been used to create a feature fusion model. A developed linear model for site-year 4 has been used for evaluating the rest of the data sets. The used model did not correlate on a significant de novo level but by changing only one parameter, it resulted in a significant correlation. The data analysis reveals that by increasing mixed features from different sensors in a model, the higher and more robust the r 2 values became. New advanced algorithms, in combination with existent map overlay approaches, have the potential of complete and weighted decision fusion, to ensure the maximum yield for each specific field condition.

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“…Continuous cropping and integrated use of organic and inorganic fertilizers increased soil C sequestration and crop yields. Balanced application of NPK fertilizers with FYM was best option for higher crop yields in maize-wheat rotation (Brar, 2015).…”

Section: Resultsmentioning

confidence: 99%