Spatial Variation in Biomass and Its Relationships to Soil Properties in the Permafrost Regions Along the Qinghai-Tibet Railway

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;

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Spatial variation of soil properties and plant colonisation on cut slopes: a case study in the semi-tropical hilly areas of China

Plant Ecology & Diversity ◽

10.1080/17550874.2015.1060544 ◽

2015 ◽

Vol 9 (1) ◽

pp. 81-88 ◽

Cited By ~ 3

Author(s):

Chunxiao Feng ◽

Yingwei Ai ◽

Zhaoqiong Chen ◽

Hao Liu ◽

Kexiu Wang ◽

...

Keyword(s):

Soil Properties ◽

Spatial Variation ◽

Plant Colonisation ◽

Cut Slopes

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Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

Italian Journal of Agronomy ◽

10.4081/ija.2017.1086 ◽

2017 ◽

pp. 21-30 ◽

Cited By ~ 3

Author(s):

Lorenzo Barbanti ◽

Josep Adroher ◽

Júnior Melo Damian ◽

Nicola Di Virgilio ◽

Gloria Falsone ◽

...

Keyword(s):

Soil Properties ◽

Spatial Variation ◽

Vegetation Index ◽

Soil Analysis ◽

Vegetation Indices ◽

Yield Potential ◽

Soil Parameters ◽

Landsat 8 ◽

High Performing ◽

Spectral Vegetation Indices

Assessing the spatial variation of soil and crop properties is the basis for site specific management of crop practices in precision agriculture applications. To this aim, proximal and remote spectral vegetation indices are increasingly replacing soil analysis. In this study the spatial variation of soil properties, proximal and remote spectral vegetation indices were compared in a winter wheat (Triticum aestivum L.) crop grown in a 4.15 ha field in northern Italy. Soil analysis (particle size distribution, pH, carbonates, C, total N, available P, exchangeable cations and electrical conductivity) was geo-referentially carried out; the proximal indices chlorophyll content by N-Tester and normalised difference vegetation index through GreenSeeker were determined in three dates during stem elongation; the remote indices PurePixelTM chlorophyll index and PurePixelTM vegetation index were determined through the Landsat 8 satellite in three dates during the same wheat stage. Dry biomass yield (DBY), grain yield (GY) and yield components were determined at harvest. Soil, proximal and remote data were submitted to principal component analysis (PCA), and the retained PCs were clustered to delineate areas at low, intermediate and high yield potential, based on soil parameters (CLUsp), proximal (CLUpi), and remote vegetation indices (CLUri). DBY and GY were significantly correlated with several soil parameters and vegetation indices. Spatial distribution of soil and crop data consistently depicted a low performing area (GY<3 Mg ha–1) and a high performing one (GY>8 Mg ha–1). CLUsp determined a lower GY difference between low and high performing area (+60%), compared to CLUpi and CLUri (almost +100%). In CLUsp and CLUpi the low and high performing area were of similar size (25 and 29% for the two respective areas in CLUsp; 25 and 33% in CLUpi), whereas in CLUri they were quite different (16 and 46%). Lastly, yield potential levels determined by vegetation indices (CLUpi and CLUri) exhibited a better degree of agreement with DBY and GY levels, than soil parameters (CLUsp). In exchange for this, the above referred soil parameters are quite consistent in time, allowing soil data to be used for more years. On concluding, PCA followed by clustering resulted in a robust delineation of field areas at different yield potential. This is the premise for developing research driven strategies of practical use.

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Spatial variation of soil properties impacted by aquaculture effluent in a small-scale mangrove

Marine Pollution Bulletin ◽

10.1016/j.marpolbul.2020.111511 ◽

2020 ◽

Vol 160 ◽

pp. 111511

Author(s):

Huaye Sun ◽

Ziying He ◽

Min Zhang ◽

Lingwei Yen ◽

Yingjie Cao ◽

...

Keyword(s):

Soil Properties ◽

Spatial Variation ◽

Small Scale ◽

Aquaculture Effluent

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Ecological controls on N<sub>2</sub>O emission in surface litter and near-surface soil of a managed grassland: modelling and measurements

Biogeosciences ◽

10.5194/bg-13-3549-2016 ◽

2016 ◽

Vol 13 (12) ◽

pp. 3549-3571 ◽

Cited By ~ 9

Author(s):

Robert F. Grant ◽

Albrecht Neftel ◽

Pierluigi Calanca

Keyword(s):

Soil Properties ◽

Spatial Variation ◽

Surface Soil ◽

N Uptake ◽

N2o Emissions ◽

N Availability ◽

Area Index ◽

Large Variability ◽

Near Surface ◽

Intensively Managed

Abstract. Large variability in N2O emissions from managed grasslands may occur because most emissions originate in surface litter or near-surface soil where variability in soil water content (θ) and temperature (Ts) is greatest. To determine whether temporal variability in θ and Ts of surface litter and near-surface soil could explain this in N2O emissions, a simulation experiment was conducted with ecosys, a comprehensive mathematical model of terrestrial ecosystems in which processes governing N2O emissions were represented at high temporal and spatial resolution. Model performance was verified by comparing N2O emissions, CO2 and energy exchange, and θ and Ts modelled by ecosys with those measured by automated chambers, eddy covariance (EC) and soil sensors on an hourly timescale during several emission events from 2004 to 2009 in an intensively managed pasture at Oensingen, Switzerland. Both modelled and measured events were induced by precipitation following harvesting and subsequent fertilizing or manuring. These events were brief (2–5 days) with maximum N2O effluxes that varied from <  1 mgNm−2h−1 in early spring and autumn to >  3 mgNm−2h−1 in summer. Only very small emissions were modelled or measured outside these events. In the model, emissions were generated almost entirely in surface litter or near-surface (0–2 cm) soil, at rates driven by N availability with fertilization vs. N uptake with grassland regrowth and by O2 supply controlled by litter and soil wetting relative to O2 demand from microbial respiration. In the model, NOx availability relative to O2 limitation governed both the reduction of more oxidized electron acceptors to N2O and the reduction of N2O to N2, so that the magnitude of N2O emissions was not simply related to surface and near-surface θ and Ts. Modelled N2O emissions were found to be sensitive to defoliation intensity and timing which controlled plant N uptake and soil θ and Ts prior to and during emission events. Reducing leaf area index (LAI) remaining after defoliation to half that under current practice and delaying harvesting by 5 days raised modelled N2O emissions by as much as 80 % during subsequent events and by an average of 43 % annually. Modelled N2O emissions were also found to be sensitive to surface soil properties. Increasing near-surface bulk density by 10 % raised N2O emissions by as much as 100 % during emission events and by an average of 23 % annually. Relatively small spatial variation in management practices and soil surface properties could therefore cause the large spatial variation in N2O emissions commonly found in field studies. The global warming potential from annual N2O emissions in this intensively managed grassland largely offset those from net C uptake in both modelled and field experiments. However, model results indicated that this offset could be adversely affected by suboptimal land management and soil properties.

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