Methods for Removing Spatial Variability from Field Research Trials

Mulla, D. J.; Bhatti, A. U.; Kunkel, Ralf

doi:10.1007/978-1-4613-8982-8_10

Cited by 27 publications

(10 citation statements)

References 4 publications

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“…We also note that grain yield had a spatial trend that yielded an autocorrelation length greater than 200 m. Consequently, grain yields from 25‐m plots would definitely not be independent. If observations from treatment experiments had to be analyzed using analysis of variance (ANOVA), a procedure like nearest neighbor analysis (NNA) (Mulla et al, 1990) would have to be applied prior to ANOVA in order to remove local trends, as was shown by Bhatti et al, 1991. Only if the variogram analysis yields a pure nugget effect after applying NNA are the assumptions underlying ANOVA met.…”

Section: Discussionmentioning

confidence: 99%

Assessing Spatial Variability in an Agricultural Experiment Station Field: Opportunities Arising from Spatial Dependence

Wendroth²,

2000

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Spatio‐temporal field soil and crop processes are important for site‐specific farming. The objectives of this study were to spatially evaluate selected soil physical and chemical properties and their relationship to wheat (Triticum aestivum L.) yield, and to discuss stochastic approaches to help identify processes underlying yield variability in heterogeneous field sites. Modified grid sampling included 330 sites including a primary transect. Soil properties measured for the Ap, E if present, and upper B horizons at each site included pH, P, Zn, Cu, exchangeable cations, percentage base saturation, cation exchange capacity, bulk density, soil water contents at −10, −33, and −1500 kPa, texture, and humic matter content. Wheat grain and straw were hand‐harvested on 1‐ by 2‐m plots centered on each site. Soil water content on the primary transect was determined by neutron attenuation on nine dates. Field and primary transect means and semivariograms for a given soil or plant parameter were similar. The range of spatial dependence or autocorrelation of soil parameters ranged from 10 m for Ap horizon depth to 100 m for −1500 kPa water content of the Ap. Base saturation and available water storage capacity were cross‐correlated with grain yield to a distance of ±15 and 12.5 m, respectively. State‐space analysis was used to develop a grain yield model using these two variables. Spearman rank correlation of the soil water content data suggests that the temporal stability of soil water storage is less for shallow than for deeper soil layers.

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Section: Discussionmentioning

confidence: 99%

Assessing Spatial Variability in an Agricultural Experiment Station Field: Opportunities Arising from Spatial Dependence

Wendroth²,

2000

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“…Agricultural and soil research usually involves 4 large plots or large numbers of treatments and, therefore, large blocks which are rarely uniform. In 5 such cases, soil heterogeneity can result in low efficiency of the randomized complete block design 6 due to a large error term (Mulla et al, 1990;Brownie et al, 1993). Likewise, when local 7 heterogeneity is spatially structured, randomization and replication alone do not ensure that all 8 treatment comparisons are made with equal precision (van Es and van Es, 1993).…”

Section: Introductionmentioning

confidence: 99%

Efficiency of an Incomplete Block Design Based on Geostatistics for Tillage Experiments

López

Arrúe

1995

Soil Science Soc of Amer J

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Spatial dependence of soil properties often reduces the power of conventional statistical methods to detect treatment differences. Control of adverse effects of soil variability is of special interest in long‐term experiments when small and slowly developing treatment effects are generally expected to occur, as in tillage research. The main objective of this study was to evaluate the ability of an incomplete block design based on geostatistical concepts to improve the precision of a conservation tillage experiment conducted at four sites in Aragón, northeast Spain. A preliminary geostatistical characterization of plow layer variability at these sites showed that, in most instances, soil water content and silt plus clay content were spatially dependent. Maps of kriged estimates of these properties were used to locate the tillage plots according to the proposed design. Using incomplete blocks of Size 2, the method estimates treatment effects by making short‐distance comparisons and ensures spatially balanced treatment contrasts through fixed comparison distances. This design was compared with a complete block design using soil and crop data obtained during the first two growing seasons of the tillage experiment. The results of a total of 1050 analysis of variance comparisons revealed that the incomplete block design was, on average, 24% more efficient than the complete block design. The use of incomplete blocks reduced the average error mean square by 33% and increased the number of cases with significant tillage treatment differences by 25% compared with the use of complete blocks.

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“…Besides these natural process some other agronomic practices like fertilization, crop rotation also influences these properties at greater extent. Variation in soil properties affect crop growth though reduction in effectiveness of uniformly applied fertilizer to the field as described by Mulla et al (1990). In past, variation in properties of soil has been determined through field experiments such as Rothamsted Classical Experiments by Johnston et al (1986) and Ecological Research Program by Risser (1991).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability pattern and mapping of selected soil properties in hilly areas of Hindukush range northern, Pakistan

Ahmad¹,

Muhammad²,

Mussarat³

et al. 2018

Eurasian Journal of Soil Science (Ejss)

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Article InfoSoil samples at 0-20 cm depth were collected from major crop areas of Hindukush mountainous range, District Chitral, extreme Northwestern Pakistan, during April 2014 to assess their physico-chemical properties and spatial distribution pattern. 103 soil samples were analyzed and maps were created by geostatistical technique of inverse distance weighting and kriging techniques using GIS and GS win-7 computer software. The soil texture ranged from silt loam to dominantly sandy loam, slightly acidic to alkaline and moderate to highly calcareous but with no salinity indication. Soil organic matter was higher than 2 % in about 75 % of samples. Soil pH, EC and lime showed slight dependence on each other with r values from 0.4 to 0.5 while OM varied independently as indicated by their lower correlation values. Semivariogram analysis showed that soil pH, lime, OM had strong spatial dependence (nugget-sill ratio, <25%) while silt, sand, EC had moderately (nugget-sill ratio, 25-75%) and clay had weakly distributed in the area. Linear, Gaussian and exponential models were used for different soil parameter based on nugget, mean prediction error and root mean square standardize prediction error values and maps were developed through extension techniques to cover all the area outside the sampling points.

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Methods for Removing Spatial Variability from Field Research Trials

Cited by 27 publications

References 4 publications

Assessing Spatial Variability in an Agricultural Experiment Station Field: Opportunities Arising from Spatial Dependence

Assessing Spatial Variability in an Agricultural Experiment Station Field: Opportunities Arising from Spatial Dependence

Efficiency of an Incomplete Block Design Based on Geostatistics for Tillage Experiments

Spatial variability pattern and mapping of selected soil properties in hilly areas of Hindukush range northern, Pakistan

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