Can spatial modeling substitute experimental design in agricultural experiments


In most situations, models that included spatial correlation were better than models with no spatial correlation, but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute

In most situations, models that included spatial correlation were better than models with no spatial correlation, but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design.Jan 1, 2019


What are the experimental design used in agriculture?

Randomized Complete Block Design. The randomized complete block design is the most commonly used design in agricultural field research.

Why do we conduct experimental research in agriculture?

Experimentation is an integral component of almost every agricultural and other scientific endeavour. Properly designed experiments not only answer all the questions of the researcher but also make efficient use of available resources while doing so.

What is experimental unit in agriculture?

An experiment unit is the smallest unit receiving a single treatment. An experimental unit may be a single apple on a tree, a branch on a tree, a whole tree, a plot of 5 consecutive trees, an entire row of trees or even a 10 acre block of trees.

What is experimental design in plant breeding?

Experimental design is the process of choosing treatments, responses, and controls, defining experimental and sample units, and determining the physical arrangement, or layout, of experiment units.

What type of research is done for the agriculture industry?

Animal immunization, artificial insemination, biological control of pests, embryo transfer, genetic engineering, hydroponics, and tissue culture are just a few areas of agricultural research.

What are the scientific methods used in agriculture?

New farming irrigation methods such as drip irrigation, stronger and more resistant pesticides, more efficient fertilizers, and newly developed seeds helped in proficient crop growth. As a result of such new improvements in agricultural methods, India experienced drastic increases in crop production.

What are the types of experimental design?

There are three primary types of experimental design:Pre-experimental research design.True experimental research design.Quasi-experimental research design.

What is the importance of experimental design?

Through accurate and precise empirical measurement and control an experimental design increases a researcher’s ability to determine causal relationships and state causal conclusions. Experimental design as a subset of scientific investigation is a popular and widely used research approach.

What is completely randomized design in agriculture?

A completely randomized design (CRD) is one where the treatments are assigned completely at random so that each experimental unit has the same chance of receiving any one treatment. For the CRD, any difference among experimental units receiving the same treatment is considered as experimental error.

What is RBD in agriculture?

Randomized Block Design. Ø It is also called RBD. Ø RBD is the most commonly used experimental design in agriculture. Ø Here the ‘local-control’ is adopted and the experimental material is grouped into homogenous subgroups. Ø Each such sub-group is called blocks.

Why RBD is used in plant breeding?

Randomization provides equal chance to all treatments for being allotted to more fertile plot as well as to less fertile plot.

What are the basic principles of experimental design?

The basic principles of experimental design are (i) Randomization, (ii) Replication, and (iii) Local Control.Randomization. Randomization is the cornerstone underlying the use of statistical methods in experimental designs. … Replication. By replication, we mean that repetition of the basic experiments. … Local Control.

How does neighbour balance and evenness of distribution design help?

Neighbour balance and evenness of distribution designs help to address user concerns in the two‐dimensional layout of agricultural field trials. This is done by minimising the occurrence of pairwise treatment plot neighbours and ensuring that the replications of treatments are spread out across rows and columns of a trial. Such considerations result in a restriction on the normal randomisation process for a row‐column design which can lead to error variance bias. In this paper, uniformity trial data is used to assess the degree of this bias for both resolvable and non‐resolvable designs. Comparisons are made with a similar investigation using Linear Variance spatial designs. This article assesses the degree of error variance bias associated with neighbour balance and evenness of distribution designs.

How does plant breeding work?

Plant breeding aims to create new varieties that outperform the parents by combining valuable traits. The breeding cycle of selection–recombination–selection–testing requires resources, time, and experience to deliver improved varieties with appropriate phenology, efficient plant type, higher yield, and better nutritional quality. Pulse breeders have used classical plant breeding methods with modest success, in terms of crop duration, grain yield, and disease resistance, to develop more than 3700 improved varieties of different pulse crops globally. However, these efforts have not achieved the large genetic gains needed to close the gap between demand and supply. Studies have identified a narrow genetic base and high proportion of variance due to environment (E) and genotype × environment (GE) interactions in the total phenotypic variance of pulse crops in multilocation environment trials (MET) as significant factors for reduced selection efficiency, as well as the lengthy breeding cycle. This chapter reviews the present status of pulse crops, production trends, past breeding progress, and the means to accelerate genetic gain. The application of modern tools and techniques of phenotyping, genotyping, experimental design, data management, statistical analysis, and digitalization and mechanization of breeding and testing pipelines is the way forward for accelerating genetic gains in pulse crops to meet the future demands of the increasing population.

What is spatial variability in agriculture?

A large amount of spatial variability in agriculture field experiments occurs as a result of spatial, temporal, and spatiotemporal variations in fertility, moisture, slope, shade, management practices, disease and pests incidence, and microclimatic variations (Borges et al. 2019; González-Barrios et al. 2019; Grondona et al. 1996; Stefanova et al. 2009 ). Effectively controlling this spatial variation within a field is necessary to produce accurate and unbiased estimates of treatment effects (Grondona et al. 1996 ). Several studies have evaluated methodologies proposed to solve this complex problem (Borges et al. 2019; González-Barrios et al. 2019; Moehring et al. 2014 ), but evaluations under real field variability for a broad set of situations are still lacking. This study evaluated ten experimental designs with and without spatial corrections in 100 sites of a uniformity trial with differing amounts of spatial variation to determine their relative abilities at controlling the spatial variation. The designs were tested in a range of scenarios including differing experiment sizes, experimental unit sizes, trait heritabilities, levels of GE, and the relationship among genotypes. The results from this study provide a guide to researchers for designing and analyzing routinely conducted experiments.

How to control spatial variation in field trials?

Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a two-dimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1 times AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments.

Which experimental design has the highest COR?

The highest COR was achieved by the fully replicated experimental designs such as the ALPHA design, SP designs, R-CD, and CRD (Tables 2 , 3, Fig. 3 ). The experimental design with the highest overall COR was the ALPHA (Tables 2 , 3, Fig. 3 ). Furthermore, the superiority of the ALPHA experimental design was more noticeable in the medium-sized experiments with low heritability, low GE, and assuming independent genotypes (i.e., one of the hardest situations tested for any model, Fig. 3 ). The SP designs performed similarly to the R-CD in all scenarios. The UNREP was the experimental design with the lowest COR in all scenarios followed by the PREP designs. When the additive relationship matrix was used, the hbox {PREP}_ { {n}} experimental designs had higher COR than the hbox {PREP}_ { {g}} experimental designs (Fig. 3 ).

What are the factors that are included in a simulated yield?

Each scenario included one level of each of the following factors: GE, experimental unit size, experiment size, trait heritability, and experimental design.

What is the difference between independent and dependent variables?

In on-farm research, the independent variable is the different treatments (practices) you are applying , and the dependent variable is the effect or outcome you are measuring.

What are the treatments for animal operations?

For animal operations, treatments might be different feed rations, type of bedding, pasture versus confinement, grazing period, nutritional supplements, or disease/parasite controls. The choices are limitless given the complexity of farming. On-farm research usually compares just two or three practices.

What caused the tomato field to be wetter than the other?

The new variety was planted in a part of the field that had better soil. One end of the field was wetter than the other and some of the tomatoes were infected with powdery mildew. Soil texture differences resulted in increased soil moisture from one end of the field to the other.


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