Geophysical and Contamination Assessment of Soil Spatial Variability for Sustainable Precision Agriculture in Omu-Aran Farm, Northcentral Nigeria

2021 ◽  
Author(s):  
Olusola Titilope Kayode ◽  
A. P. Aizebeokhai ◽  
A. M. Odukoya
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Contamination Assessment ◽  
Soil Spatial Variability

scholarly journals Temporal stability of electrical conductivity in a sandy soil

2016 ◽  
Vol 30 (3) ◽  
pp. 349-357 ◽  
Author(s):  
Aura Pedrera-Parrilla ◽  
Eric C. Brevik ◽  
Juan V. Giráldez ◽  
Karl Vanderlinden
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Precision Agriculture ◽  
Temporal Stability ◽  
Principal Component ◽  
Model Parameters ◽  
Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

scholarly journals Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 194
Author(s):  
Mohamed A. E. AbdelRahman ◽  
Yasser M. Zakarya ◽  
Mohamed M. Metwaly ◽  
Georgios Koubouris
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Nutrient Management ◽  
Prediction Method ◽  
Available Phosphorus ◽  
Detailed Knowledge ◽  
Agricultural Planning ◽  
Management Recommendations ◽  
Soil Spatial Variability

Detailed knowledge of soil properties is fundamentally important for optimizing agriculture practices and management. Meanwhile, the spatial distribution of soil physicochemical properties is considered a fundamental input of any sustainable agricultural planning. In the present study, ordinary kriging, regression kriging and IDW were chosen for deciphering soil spatial variability and mapping soil properties in a reclaimed area of the Behera Governorate of Egypt where soil arose from two different types, one sandstone and the other limestone. Geostatistics were used to show the interrelationships and conditions of soil properties (available phosphorus, potassium and nitrogen, EC, pH, Sp, ESP, CEC, OC, SAR, and CaCO3). The results of mapping spatial soil variability by Geostatistics could be used for precision agriculture applications. Based on the soil test results, nutrient management recommendations should be applied regarding variable rates of fertilizers. The performance of the maps was evaluated using Mean square error (MSE). Inverse distance weight (IDW) showed higher efficiency than Kriging as a prediction method for mapping the studied soil properties in the study area. The results of the present study suggest that the application of the selected fit model worldwide in any relevant study of soil properties of different geological sources is feasible.

Characterizing Soil Spatial Variability for Precision Agriculture Using Geophysical Measurements

2004 ◽  
Author(s):  
Dennis L. Corwin ◽  
Scott M. Lesch ◽  
Peter J. Shouse ◽  
Richard Soppe ◽  
James E. Ayars
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Soil Spatial Variability ◽  
Geophysical Measurements

scholarly journals The 13C Discrimination of Crops Identifies Soil Spatial Variability Related to Water Shortage Vulnerability

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1691
Author(s):  
Jan Haberle ◽  
Renata Duffková ◽  
Ivana Raimanová ◽  
Petr Fučík ◽  
Pavel Svoboda ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Water ◽  
Precision Agriculture ◽  
Spring Barley ◽  
Water Shortage ◽  
Soil Conditions ◽  
N Content ◽  
Water Capacity ◽  
13C Discrimination ◽  
Soil Spatial Variability

Spatial variability of crop growth and yields is the result of many interacting factors. The contribution of the factors to variable yields is often difficult to separate. This work studied the relationships between the 13C discrimination (Δ13C) of plants and the spatial variability of field soil conditions related to impacts of water shortage on crop yield. The 13C discrimination, the indicator of water shortage in plants, 15N (δ15N) discrimination, and nitrogen (N) content were determined in grains of winter wheat, spring barley, and pea. The traits were observed at several dozens of grid spots in seven fields situated in two regions with different soil and climate conditions between the years 2017 and 2019. The principles of precision agriculture were implemented in some of the studied fields and years by variable rate nitrogen fertilization. The Δ13C significantly correlated with grain yields (correlation coefficient from 0.66 to 0.94), with the exception of data from the wetter year 2019 at the site with higher soil water capacity. The effect of drought was demonstrated by statistically significant relationships between Δ13C in dry years and soil water capacity (r from 0.46 to 0.97). The significant correlations between Δ13C and N content of seeds and soil water capacity agreed with the expected impact of water shortage on plants. The 13C discrimination of crop seeds was confirmed as a reliable indicator of soil spatial variability related to water shortage. Stronger relationships were found in variably fertilized areas.

Characterizing Soil Spatial Variability For Precision Agriculture Using Geophysical Measurements

2004 ◽  
Author(s):  
Dennis L. Corwin ◽  
Scott M. Lesch ◽  
Peter J. Shouse ◽  
Richard Soppe ◽  
James E. Ayars
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Soil Spatial Variability ◽  
Geophysical Measurements

Soil spatial variability in the vineyards of La Rioja PDOC (Spain)

2021 ◽  
pp. 1-11
Author(s):  
Victoria Iñigo ◽  
Álvaro Marín ◽  
María S. Andrades ◽  
Raimundo Jiménez-Ballesta
Keyword(s):  
Spatial Variability ◽  
La Rioja ◽  
Soil Spatial Variability

Soil spatial variability characterization: Delineating index-based management zones in salt-affected agroecosystem of India

2021 ◽  
Vol 296 ◽  
pp. 113243
Author(s):  
Arijit Barman ◽  
Parvender Sheoran ◽  
Rajender Kumar Yadav ◽  
Ramesh Abhishek ◽  
Raman Sharma ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Management Zones ◽  
Soil Spatial Variability

Needle Probe Application for High-Resolution Assessment of Soil Spatial Variability in the Centrifuge

2005 ◽  
Author(s):  
Zhihua Li ◽  
Bruce L. Kutter ◽  
Daniel W. Wilson ◽  
Kenneth Sprott ◽  
Jong-Sub Lee ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Needle Probe ◽  
Soil Spatial Variability
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