Spatial Analysis of Agricultural Field Experiments

Stringer, J. K.; Smith, Alison; Cullis, Brian R

doi:10.1002/9781118147634.ch3

Cited by 7 publications

(5 citation statements)

References 52 publications

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“…Spatial variation also depends on experimental design factors that determine trial layout, such as plot size and shape (Zhang et al, 1994) and block size and shape (Fagroud and Van Meirvenne, 2002). Spatial variation is typically controlled through proper experimental design and statistical analyses; these two approaches are often referred to as a priori and a posteriori techniques, respectively (Gezan et al, 2010;Stringer et al, 2012). Spatial variation is typically controlled through proper experimental design and statistical analyses; these two approaches are often referred to as a priori and a posteriori techniques, respectively (Gezan et al, 2010;Stringer et al, 2012).…”

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confidence: 99%

“…Unless taken into account, spatial variability in yield trials can severely limit the precision of yield estimates. Spatial variation is typically controlled through proper experimental design and statistical analyses; these two approaches are often referred to as a priori and a posteriori techniques, respectively (Gezan et al, 2010;Stringer et al, 2012). The a priori approach is model driven, where the model is predetermined on the basis of experimental design, whereas the a posteriori approach is typically data driven, where different covariates or covariance structures are modeled to account for spatial variation and an adequate model is determined on the basis of some goodness-of-fit criteria.…”

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confidence: 99%

“…For evaluating large numbers of candidate cultivars, RCBD experiments are not effective in creating a large homogeneous group of experimental units. To overcome this problem, several incomplete block designs, such as square and rectangular lattices, a-designs, and row-column designs, are proposed for large experiments (Stringer et al, 2012). Despite high relative efficiencies reported for these designs, their application in yield trials is limited because of several logistical constraints.…”

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Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Sripathi

Conaghan

Grogan³

et al. 2017

Crop Science

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Spatial analyses of yield trials allow adjustment of cultivar means for spatial variation, improving the statistical precision of yield estimation. While the relative efficiency of spatial analysis has been frequently reported in several yield trials, its application to long‐term Lolium spp. forage yield trials has not been characterized. The objective of this study was to evaluate the trend analysis, nearest‐neighbor analysis (NNA), and correlated error (CE) models for their ability to account for spatial variability in 138 Lolium spp. forage yield trials. This case study was performed on data from five locations and 11 yr (2001–2011) using randomized complete block design (RCBD) trials conducted by the Department of Agriculture, Food and Marine (DAFM) in Ireland. The relative efficiencies of trend, NNA, and CE models compared with RCBD models were 129, 143, and 193% for analysis by trial × year, and 121, 125, and 171% for analysis by trial, respectively. When the top one, two, three, four, or five cultivar(s) were compared between CE and RCBD models, the agreement between two models to find common cultivars varied from 66% for the top cultivar to 28% for the top five cultivars. Using CE models, four replicates were sufficient to detect mean yield differences between cultivars of 7% of the mean and 80% power. Spatial analysis should be added to the routine DAFM testing programs, not only to improve the precision of yield estimates, but also to reduce the risk of missing potential candidate cultivars, given the existence of spatial variation.

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Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Sripathi

Conaghan

Grogan³

et al. 2017

Crop Science

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“…The sugar yield (t ha -1 ) was calculated using the formula: Cane yield (t ha -1 ) × CCS % /100. The data obtained from the study were statistically analyzed at 5 % level of significance as per procedure given by Sundaraj et al (1972).…”

Section: Treatmentsmentioning

confidence: 99%

Effect of organic manure and zero budget technique on soil fertility and productivity of sugarcane plant and ratoon crop

2022

APSR

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A field experiment was conducted on clay loam soils at Regional Research Station, Chaudhary Charan Singh Haryana Agricultural University, Karnal, Haryana for three year (2018-2021) to investigate the effect of different organic treatments such as farmyard manure (FYM), bio fertilizers (BF) and green manuring (GM) with Sesbania or trash mulching (TM) on sugarcane yield, quality and soil fertility status. Cane length, cane girth, number of millable canes and commercial soluble sugar percent increased with the application of FYM, BF and GM/TM as compared to zero budget technique. The treatment that received 30 t FYM ha-1 +BF+GM/TM+ integrated pest management (IPM) recorded 15.8 and 12.3 % increase in cane yield over zero budget technique in plant and ratoon crops, respectively. Both in plant and ratoon crop, higher available nitrogen (145.7 and 147.8 kg ha-1 ), phosphorus (18.57 and 18.43 kg ha-1 ) and potassium (406.8 and 412.8 kg ha-1 ) were recorded where 30 t FYM ha-1 was applied along with BF+GM/TM+IPM. The application of 30 t FYM ha - 1 +BF+GM/TM+ IPM was found at par with treatment 20 t FYM ha-1 +BF+GM+IPM for plant crop and 25 t FYM ha-1 +BF+TM + IPM for ratoon crop. The treatment 20 or 25 t FYM ha-1 + BF + GM/TM + IPM were at par with the RDF treatment. The cane yield and juice quality obtained as a result of application of 20t FYM ha-1 + BF+GM+IPM for sugarcane plant crop and 25t FYM ha-1 +BF+TM+IPM for sugarcane ratoon crop was comparable with that of RDF.

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“…In this design treatments must be arranged all together in each group, but the number of treatments usually cannot exceed 20, because heterogeneity increases within the block and consequently experimental error increases too (Stroup et al, 1994;Stringer et al, 2012). To overcome this problem, a new category of designs has been developed with each group divided into smaller more homogeneous subgroups that do not contain all treatments.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of experimental designs in durum wheat trials

Katsileros

Koukouvinos

2015

Biometrical Letters

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SUMMARYVariability among experimental plots may be a relevant problem in field genotype experiments, especially when a large number of entries are involved. Four field trials on 24 durum wheat genotypes were conducted in 2013/14 in order to evaluate the efficiency of Incomplete Block, Alpha and Augmented designs in comparison with the traditional Randomized Complete Block Design (RCBD). The results showed that the RCBD can be replaced by an Alpha design, which provides better control of variability among the experimental units when the number of treatments to be tested in an experiment exceeds twenty. The ranking of the genotypes across the four designs was not constant.

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Spatial Analysis of Agricultural Field Experiments

Cited by 7 publications

References 52 publications

Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Spatial Variability Effects on Precision and Power of Forage Yield Estimation

Effect of organic manure and zero budget technique on soil fertility and productivity of sugarcane plant and ratoon crop

Evaluation of experimental designs in durum wheat trials

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