Using Soil Attributes and Gis for Interpretation of Spatial Variability in Yield

Bakhsh, Allah; Colvin, T. S.; Jaynes, Dan B.; Kanwar, Rameshwar S.; Tim, U. Sunday

doi:10.13031/2013.2976

Cited by 47 publications

(43 citation statements)

References 29 publications

(20 reference statements)

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“…Crop yield variability within a field may be due to intrinsic factors such as soil type, soil moisture, and nutrient potential as well as the extrinsic factors such as climate and management practices (Lamb et al, 1997;Bakhsh et al, 2000). Climatic variables are the most dominating factors that also cause temporal variability of crop yields within a field from year to year.…”

Section: Introductionmentioning

confidence: 99%

“…GIS is a powerful tool for determining the integrated effects of the various soil and landscape data layers with crop yield patterns (Bakhsh et al, 2000). The effects of soil and topographic attributes on yield variation can be perceived better when data layers of these attributes are overlaid (Silva and Alexandre, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Crop yield has also been considered a good indicator for delineation of stable management zones (Bakhsh et al, 2000). Cluster and discriminant analyses have been used to classify the crop yield data into meaningful groups and study the contribution of various soil and landscape attributes in discriminating these clusters, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Role of landscape and hydrologic attributes in developing and interpreting yield clusters

Bakhsh

Kanwar

Malone³

2007

Geoderma

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Management of agricultural fields based on yield patterns may help farmers adopt environmentally friendly farming practices. Our objective was to investigate landscape and hydrologic attributes that affect spatial clusters of corn (Zea mays L.)-soybean (Glycine max L.) yields. The study was conducted at Iowa State University's northeastern research center near Nashua, Iowa, from 1993 to 1998. The yield data, normalized for annual climatic variability, were used in cluster and discriminant analysis, and the landscape and hydrologic data were overlain using ArcGIS software. Three clusters of low, medium and high categories were formed using 10 iterations with zero convergence options and satisfying the R 2 , pseudo F-statistic and cubic clustering criteria. The spatial clusters, however, varied greatly over space and time domain for the study period. The map overlay analysis using ArcGIS showed that high yield clusters were affected by soil and lower elevation levels in the below average precipitation year of 1994. The annual normalized subsurface drainage volume, nitrate leaching losses, soil type and topographic attributes of slope, aspect, and curvature were used in stepwise discriminant analysis to identify the variables significantly related to the clusters. Soil and topographic attributes of curvature and aspect contributed significantly in cluster formations for four of the six years at P ≤ 0.15. The results suggest that cluster and discriminant analysis can be useful for identification of soil and topographic attributes affecting corn and soybean yield patterns, which can help in delineation of management zones for site specific management practices. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. AbstractManagement of agricultural fields based on yield patterns may help farmers adopt environmentally friendly farming practices. Our objective was to investigate landscape and hydrologic attributes that affect spatial clusters of corn (Zea mays L.)-soybean (Glycine max L.) yields. The study was conducted at Iowa State University's northeastern research center near Nashua, Iowa, from 1993 to 1998. The yield data, normalized for annual climatic variability, were used in cluster and discriminant analysis, and the landscape and hydrologic data were overlain using ArcGIS software. Three clusters of low, medium and high categories were formed using 10 iterations with zero convergence options and satisfying the R 2 , pseudo F-statistic and cubic clustering criteria. The spatial clusters, however, varied greatly over space and time domain for the study period. The map overlay analysis using ArcGIS showed that high yield clusters were affected by soil and lower elevation levels in the below average precipitation year of 1994. The annual normalized subsurface drainage volume, nitrate leaching losses, soil type and topographic attributes of slope, aspect, and curvature were used in ste...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Role of landscape and hydrologic attributes in developing and interpreting yield clusters

Bakhsh

Kanwar

Malone³

2007

Geoderma

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“…What information do we need to make an appropriate decision? Bakhsh et al [3] used a statistical approach to characterize the spatio-temporal variability within a field. They found that overall, yield variability was not stable spatially or temporally.…”

Section: Introductionmentioning

confidence: 99%

Within Field Variability in Wheat Grain Yields Over Nine Years in Oklahoma

Washmon

Solie

Raun

et al. 2002

Journal of Plant Nutrition

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Wheat grain yield levels are known to vary from 1 year to the next and this variability is frequently attributed to differences in rainfall. However, within field variability of wheat grain yields as a function of time has not been extensively evaluated. Wheat grain yields from selected fields in Oklahoma were monitored over a 9-year period using satellite imagery. Yields for each 25 m Â 25 m area within each field were estimated from NDVI measurements obtained from LANDSAT scenes in north-central Oklahoma. Minimum, maximum, mean, standard deviation, and coefficient of variation (CV) values were collected from six locations from 1991 to 1999. Coefficient of variations for wheat grain yield ranged between 16 and 38% for the same field. The wide range in CVs could be partly explained by the changes in

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“…Some studies show a correlation between grain yield and soil physical characteristics (SWAN et al, 1987;BAKHSH et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

CORRELATION BETWEEN CLAY CONTENT AND SEED DEPTH IN NO-TILLAGE CORN (Zea mays L.)

Neto

Garbuio²,

Gimenez

et al. 2008

Revista Brasileira De Engenharia De Biossistemas

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The region of Campos Gerais, in the State of Paraná, is leader in grain yields and state-of-the-art no-tillage farming. The widely adopted no-tillage system tends to increment soil variability. There are several studies about spatial variability of soil characteristics affecting grain yield, but not enough attention has been given to the variability resulting of human actions. The objective of this study was to evaluate the correlation of soil clay content and the depth of placement of corn seeds on areas under no-till systems management. The four selected cornfields areas for the study are property of local farm cooperatives associates. The points for evaluation were defined considering the local mapping of soil texture. Clay content varied from 94 to 489 g kg -1 on Plot 1; from 222 to 414 g kg -1 on Plot 2; from 269 to 509 of rows to be considered for plant distribution and seed depth analyses. The coefficient of variation (CV) of plant distribution was between 23 and 56%. For seed depth, the CV was between 18 and 34%. The regression analysis showed high coefficients of determination (r 2 ) for plots 1 and 2 (r 2 =0,85 and 0,83). The clay content was generally higher on plots 3 and 4. In this case, the analysis of variance was not significant, and the coefficients of determination were low (r 2 =0,22 and 0,01). Results indicate that clay content values may be used to delimit g kg -1 on plot 3; and from 368 to 698 g kg -1 on plot 4. The type of planter determined the number management zones on the field, where the depth of seed placing in the planting process can be regulated in different ways. Keywords

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Using Soil Attributes and Gis for Interpretation of Spatial Variability in Yield

Cited by 47 publications

References 29 publications

Role of landscape and hydrologic attributes in developing and interpreting yield clusters

Role of landscape and hydrologic attributes in developing and interpreting yield clusters

Within Field Variability in Wheat Grain Yields Over Nine Years in Oklahoma

CORRELATION BETWEEN CLAY CONTENT AND SEED DEPTH IN NO-TILLAGE CORN (Zea mays L.)

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