scholarly journals Delineating site-specific management zones for pH-induced iron chlorosis

2008 ◽  
Vol 9 (1-2) ◽  
pp. 71-84 ◽  
Author(s):  
T. Kyaw ◽  
R. B. Ferguson ◽  
V. I. Adamchuk ◽  
D. B. Marx ◽  
D. D. Tarkalson ◽  
...  
2003 ◽  
Vol 95 (2) ◽  
pp. 303 ◽  
Author(s):  
Cinthia K. Johnson ◽  
David A. Mortensen ◽  
Brian J. Wienhold ◽  
John F. Shanahan ◽  
John W. Doran

2012 ◽  
Vol 58 (10) ◽  
pp. 1075-1090 ◽  
Author(s):  
Hou-Long Jiang ◽  
Guo-Shun Liu ◽  
Shu-Duan Liu ◽  
En-Hua Li ◽  
Rui Wang ◽  
...  

2006 ◽  
Vol 98 (2) ◽  
pp. 407-415 ◽  
Author(s):  
A. Hornung ◽  
R. Khosla ◽  
R. Reich ◽  
D. Inman ◽  
D. G. Westfall

2017 ◽  
Vol 143 (9) ◽  
pp. 04017037 ◽  
Author(s):  
Aghil Yari ◽  
Chandra A. Madramootoo ◽  
Shelley A. Woods ◽  
Viacheslav I. Adamchuk ◽  
Hsin-Hui Huang

2015 ◽  
Vol 39 (1) ◽  
pp. 59-70
Author(s):  
Matshwene Moshia ◽  
Raj Khosla ◽  
Dwayne Westfall ◽  
Jessica Davis ◽  
Robin Reich

2015 ◽  
Vol 154 (2) ◽  
pp. 273-286 ◽  
Author(s):  
H. U. FARID ◽  
A. BAKHSH ◽  
N. AHMAD ◽  
A. AHMAD ◽  
Z. MAHMOOD-KHAN

SUMMARYDelineating site-specific management zones within fields can be helpful in addressing spatial variability effects for adopting precision farming practices. A 3-year (2008/09 to 2010/11) field study was conducted at the Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the most important soil and landscape attributes influencing wheat grain yield, which can be used for delineating management zones. A total of 48 soil samples were collected from the top 300 mm of soil in 8-ha experimental field divided into regular grids of 24 × 67 m prior to sowing wheat. Soil and landscape attributes such as elevation, % of sand, silt and clay by volume, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus (P) were included in the analysis. Artificial neural network (ANN) analysis showed that % sand, % clay, elevation, soil N and soil EC were important variables for delineating management zones. Different management zone schemes ranging from three to six were developed and evaluated based on performance indicators using Management Zone Analyst (MZA V0·1) software. The fuzziness performance index (FPI) and normalized classification entropy NCE indices showed minimum values for a four management zone scheme, indicating its appropriateness for the experimental field. The coefficient of variation values of soil and landscape attributes decreased for each management zone within the four management zone scheme compared to the entire field, which showed improved homogeneity. The evaluation of the four management zone scheme using normalized wheat grain yield data showed distinct means for each management zone, verifying spatial variability effects and the need for its management. The results indicated that the approach based on ANN and MZA software analysis can be helpful in delineating management zones within the field, to promote precision farming practices effectively.


2013 ◽  
Vol 93 (2) ◽  
pp. 205-218 ◽  
Author(s):  
Nahuel Raúl Peralta ◽  
José Luis Costa ◽  
Mónica Balzarini ◽  
Hernán Angelini

Peralta, N. R., Costa, J. L., Balzarini, M. and Angelini, H. 2013. Delineation of management zones with measurements of soil apparent electrical conductivity in the southeastern pampas. Can. J. Soil Sci. 93: 205–218. Site-specific management demands the identification of subfield regions with homogeneous characteristics (management zones). However, determination of subfield areas is difficult because of complex correlations and spatial variability of soil properties responsible for variations in crop yields within the field. We evaluated whether apparent electrical conductivity (ECa) is a potential estimator of soil properties, and a tool for the delimitation of homogeneous zones. ECamapping of a total of 647 ha was performed in four sites of Argentinean pampas, with two fields per site composed of several soil series. Soil properties and ECawere analyzed using principal components (PC)–stepwise regression and ANOVA. The PC–stepwise regression showed that clay, soil organic matter (SOM), cation exchange capacity (CEC) and soil gravimetric water content (θg) are key loading factors, for explaining the ECa(R2≥0.50). In contrast, silt, sand, extract electrical conductivity (ECext), pH values and [Formula: see text]-N content were not able to explain the ECa. The ANOVA showed that ECameasurements successfully delimited three homogeneous soil zones associated with spatial distribution of clay, soil moisture, CEC, SOM content and pH. These results suggest that field-scale ECamaps have the potential to design sampling zones to implement site-specific management strategies.


Geoderma ◽  
2014 ◽  
Vol 232-234 ◽  
pp. 381-393 ◽  
Author(s):  
Rong-Jiang Yao ◽  
Jing-Song Yang ◽  
Tong-Juan Zhang ◽  
Peng Gao ◽  
Xiang-Ping Wang ◽  
...  

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