Spatial variation in soil properties and crop yield on stone bund terraces in southwest Ethiopia

2021 ◽  
Author(s):  
Kebede Wolka ◽  
Birhanu Biazin ◽  
Vegard Martinsen ◽  
Jan Mulder
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Crop Yield ◽  
Southwest Ethiopia

scholarly journals Characterization of tillage effects on the spatial variation of soil properties using ground-penetrating radar and electromagnetic induction

Geoderma ◽  
2013 ◽  
Vol 207-208 ◽  
pp. 310-322 ◽  
Author(s):  
François Jonard ◽  
Mohammad Mahmoudzadeh ◽  
Christian Roisin ◽  
Lutz Weihermüller ◽  
Frédéric André ◽  
...  
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Ground Penetrating Radar ◽  
Electromagnetic Induction ◽  
Ground Penetrating

Effects of sewage wastewater irrigation on soil properties, crop yield and environment

2012 ◽  
Vol 103 ◽  
pp. 100-104 ◽  
Author(s):  
P.K. Singh ◽  
P.B. Deshbhratar ◽  
D.S. Ramteke
Keyword(s):  
Soil Properties ◽  
Crop Yield ◽  
Wastewater Irrigation

scholarly journals Geostatistical prediction of clay percentage based on soil survey data

2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Survey Data ◽  
Clay Content ◽  
Spherical Model ◽  
Simulation Method ◽  
Soil Survey ◽  
Management Zones ◽  
Additional Information ◽  
Clay Percentage

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.;

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

2015 ◽  
Vol 9 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Chunxiao Feng ◽  
Yingwei Ai ◽  
Zhaoqiong Chen ◽  
Hao Liu ◽  
Kexiu Wang ◽  
...  
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Plant Colonisation ◽  
Cut Slopes

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

2017 ◽  
pp. 21-30 ◽  
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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