Simultaneous Tests and Confidence Intervals for the Evaluation of Agricultural Field Trials

2004 ◽  
Vol 96 (5) ◽  
pp. 1323-1330 ◽  
Author(s):  
Cornelia Frömke ◽  
Frank Bretz
Keyword(s):  
Confidence Intervals ◽  
Field Trials ◽  
Agricultural Field

scholarly journals Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field

Science ◽  
2019 ◽  
Vol 363 (6422) ◽  
pp. eaat9077 ◽  
Author(s):  
Paul F. South ◽  
Amanda P. Cavanagh ◽  
Helen W. Liu ◽  
Donald R. Ort
Keyword(s):  
Quantum Yield ◽  
Crop Yield ◽  
Metabolic Pathways ◽  
Biomass Productivity ◽  
Field Trials ◽  
Agricultural Field ◽  
Bisphosphate Carboxylase ◽  
Transgenic Lines ◽  
Glycolate Metabolism ◽  
Pathway Flux

Photorespiration is required in C3 plants to metabolize toxic glycolate formed when ribulose-1,5-bisphosphate carboxylase-oxygenase oxygenates rather than carboxylates ribulose-1,5-bisphosphate. Depending on growing temperatures, photorespiration can reduce yields by 20 to 50% in C3 crops. Inspired by earlier work, we installed into tobacco chloroplasts synthetic glycolate metabolic pathways that are thought to be more efficient than the native pathway. Flux through the synthetic pathways was maximized by inhibiting glycolate export from the chloroplast. The synthetic pathways tested improved photosynthetic quantum yield by 20%. Numerous homozygous transgenic lines increased biomass productivity between 19 and 37% in replicated field trials. These results show that engineering alternative glycolate metabolic pathways into crop chloroplasts while inhibiting glycolate export into the native pathway can drive increases in C3 crop yield under agricultural field conditions.

scholarly journals Flexible modelling of spatial variation in agricultural field trials with the R package INLA

2019 ◽  
Vol 132 (12) ◽  
pp. 3277-3293 ◽  
Author(s):  
Maria Lie Selle ◽  
Ingelin Steinsland ◽  
John M. Hickey ◽  
Gregor Gorjanc
Keyword(s):  
Spatial Variation ◽  
Open Source ◽  
Field Trial ◽  
Spatial Models ◽  
R Package ◽  
Field Trials ◽  
Wheat Breeding ◽  
Genetic Effects ◽  
Agricultural Field ◽  
Combining Data

Abstract Key message Established spatial models improve the analysis of agricultural field trials with or without genomic data and can be fitted with the open-source R package INLA. Abstract The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ($$\mathrm{AR1} \otimes \mathrm{AR1}$$ AR 1 ⊗ AR 1 ) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the $$\mathrm{AR1} \otimes \mathrm{AR1}$$ AR 1 ⊗ AR 1 and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA.

scholarly journals FIELDimageR: An R package to analyze orthomosaic images from agricultural field trials

tppj ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Filipe Inácio Matias ◽  
Maria V. Caraza‐Harter ◽  
Jeffrey B. Endelman
Keyword(s):  
R Package ◽  
Field Trials ◽  
Agricultural Field

Analysis of Agricultural Field Trials in the Presence of Outliers and Fertility Jumps

Biometrics ◽  
1994 ◽  
Vol 50 (3) ◽  
pp. 764 ◽  
Author(s):  
Ross H. Taplin ◽  
Adrian E. Raftery
Keyword(s):  
Field Trials ◽  
Agricultural Field

scholarly journals Julian Ernst Besag. 26 March 1945—6 August 2010

2018 ◽  
Vol 64 ◽  
pp. 27-50
Author(s):  
Peter J. Diggle ◽  
Peter J. Green ◽  
Bernard W. Silverman
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Lattice Models ◽  
Field Trials ◽  
Nearest Neighbour ◽  
Agricultural Field ◽  
Lattice Systems ◽  
Markov Random ◽  
Texture Generation

Julian Besag was an outstanding statistical scientist, distinguished for his pioneering work on the statistical theory and analysis of spatial processes, especially conditional lattice systems. His work has been seminal in statistical developments over the last several decades ranging from image analysis to Markov chain Monte Carlo methods. He clarified the role of auto-logistic and auto-normal models as instances of Markov random fields and paved the way for their use in diverse applications. Later work included investigations into the efficacy of nearest-neighbour models to accommodate spatial dependence in the analysis of data from agricultural field trials, image restoration from noisy data, and texture generation using lattice models.

scholarly journals Efficiency of Alpha Lattice Design in Rice Field Trials in Pakistan

2010 ◽  
Vol 3 (1) ◽  
pp. 91 ◽  
Author(s):  
M. Kashif ◽  
M. I. Khan ◽  
M. Arif ◽  
M. Anwer ◽  
M. Ijaz
Keyword(s):  
Standard Error ◽  
Relative Efficiency ◽  
Rice Field ◽  
Field Experiments ◽  
Field Trials ◽  
Block Designs ◽  
Agricultural Field ◽  
Lattice Design ◽  
Homogeneous Block ◽  
Average Standard Error

Two rice trials were conducted from 2005 to 2006 in rice research institute, Kala Shah Kako Pakistan to evaluate the efficiency of alpha lattice design in field experiments. The average standard error of difference between genotypes mean is used to calculate relative efficiency of alpha lattice design. Both experiments clearly identified the advantages of small blocks. The average gain in efficiency was 119% with maximum 128%. Mean ranks comparison for both randomized complete block and alpha lattice design were performed. It was observed that the ranks were not constant across the experiments. The results emphasize that the traditional randomized complete block designs (RCBD) should be replaced by alpha lattice in the agricultural field experiments when number of varieties to be tested in an experiment increases to more than five or ten. In such a situation finding a homogeneous block is quite difficult in field experiments.Keywords: Rice; Alpha lattice design; RCBD; Pakistan.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.4773                 J. Sci. Res. 3 (1), 91-95 (2011)

scholarly journals Evaluation of Relative Efficiency of Alpha Lattice Design and Cluster Analysis of Twenty Spring Triticale Field Experiments

2020 ◽  
Author(s):  
R. N.F. Abdelkawy ◽  
A. Z. Turbayev ◽  
A. A. Soloviev
Keyword(s):  
Cluster Analysis ◽  
Relative Efficiency ◽  
Field Experiments ◽  
Field Trials ◽  
High Yield ◽  
Agricultural Field ◽  
Lattice Design ◽  
Spring Triticale ◽  
And Cluster Analysis ◽  
1000 Grain Weight

Twenty genotypes triticale were evaluated in an alpha lattice design and RCBD for eight characters to a comparison of the relative efficiency (RE) of alpha-lattice design and RCBD. Two experiments were analyzed according to alpha lattice design and RCB design. Average estimated (RE) was 12.97, 5.93, 21.79, 35.50, 21.53, 23.96 and 26.69% for number of plants / m2, tillering fertility, plant height (cm), spike length (cm), number of spikelets / spike, 1000-grain weight (g) and grain yield (g / m2), respectively, the high precision is obtained significantly to estimate treatment effects indicating that using an alpha lattice design in place of RCBD. Mean comparisons for both RCBD and alpha lattice design were performed and two designs confirmed to Dublet, Ulyana, 131/1656 and C259 genotypes were the highest yield (g / m2). Cluster analysis showed that genotypes were isolated into three principle groups and one of these contains one variety (Dublet). This variety was characterized by a high yield over two years. The results showed that alpha lattice more efficient and it can be used it instead of traditional RCB design in the agricultural field trials.

scholarly journals Tensor P-Spline Smoothing for Spatial Analysis of Plant Breeding Trials

2021 ◽  
Author(s):  
Hans-Peter Piepho ◽  
Martin Boer ◽  
Emlyn R. Williams
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Null Space ◽  
Spatial Interaction ◽  
Natural Extension ◽  
Covariance Structure ◽  
Spatial Modelling ◽  
Field Trials ◽  
Agricultural Field ◽  
Randomized Field Trials

Large agricultural field trials may display irregular spatial trends that cannot be fully captured by a purely randomization-based analysis. For this reason, paralleling the development of analysis-of-variance procedures for randomized field trials, there is a long history of spatial modelling for field trials, starting with the early work of Papadakis on nearest neighbour analysis, which can be cast in terms of first or second differences among neighbouring plot values. This kind of spatial modelling is amenable to a natural extension using P-splines, as has been demonstrated in recent publications in the field. Here, we consider the P-spline framework, focussing on model options that are easy to implement in linear mixed model packages. Two examples serve to illustrate and evaluate the methods. A key conclusion is that first differences are rather competitive with second differences. A further key observation is that second differences require special attention regarding the representation of the null space of the smooth terms for spatial interaction, and that an unstructured variance-covariance structure is required to ensure invariance to translation and rotation of eigenvectors associated with that null space. We develop a strategy that permits fitting this model with ease, but the approach is more demanding than that needed for fitting models using first differences. Hence, even though in other areas second differences are very commonly used in the application of P-splines, our main conclusion is that with field trials first differences have advantages for routine use.

Sign in / Sign up

Export Citation Format

Share Document