Factorial designs with check in pesticide research

Published at December 15, 2023 ·  6 min read

In pesticide research or, in general, agriculture research, we very commonly encouter experiments with two/three crossed factors and some other treatment that is not included in the factorial structure. For example, let’s consider an experiment with two herbicides (rimsulfuron and dicamba) at two rates (40 and 60 g/ha for rimsulfuron and 0.6 and 1 kg/ha for dicamba) and with four adjuvant treatments (surfactant, frigate, mineral oil and no adjuvant). Apart from this fully crossed structure, we need to introduce, at least, an untreated control and a hand-weeded control....

Regression analyses with common checks in pesticide research

Published at December 15, 2023 ·  4 min read

In pesticide research or, in general, agriculture research, we very commonly encounter experiments with, e.g., several herbicides tested at different doses and in different conditions. For these experiments, the untreated control is always added and, of course, such control is common to all herbicides. For example, in another post (see here) we have considered an experiment with two herbicides (rimsulfuron and dicamba) at two rates (40 and 60 g/ha for rimsulfuron and 0....

Repeated measures and subsampling with perennial crops

Published at December 4, 2023 ·  5 min read

In a recent post, I have talked about repeated measures, for a case where measurements were taken repeatedly in the same plots across years see here. Previously, in another post, I had talked about subsampling, for a case where several random samples were taken from the same plot see here. Repeated measures and subsampling are vastly different: in the first case I am specifically interested in the ‘evolution’ of the response over time (or space, sometimes)....

Back-transformations with emmeans()

Published at November 30, 2023 ·  5 min read

I am one of those old guys who still uses the stabilising transformations, when the data do not conform to the basic assumptions for ANOVA. Indeed, apart from counts and proportions, where GLMs can be very useful, I have not yet found a simple way to deal with heteroscedasticity for continuous variables, such as yield, weight, height and so on. Yes, I know, Generalised Least Squares (GLS) can be useful to fit heteroscedastic models, but I would argue that stabilising transformations are, conceptually, very much simpler and they can be easily thought to PhD students and practitioners, with only a basic level of knowledge about statistics....

Designed experiments with replicates: Principal components or Canonical Variates?

Published at November 2, 2023 ·  16 min read

A few days ago, a colleague of mine wanted to hear my opinion about what multivariate method would be the best for a randomised field experiment with replicates. We had a nice discussion and I thought that such a case-study might be generally interesting for the agricultural sciences; thus, I decided to take my Apple Mac-Book PRO, sit down, relax and write a new post on this matter. My colleague’s research study was similar to this one: a randomised block field experiment (three replicates) with 16 durum wheat genotypes, which was repeated in four years....

GGE analyses for multi-environment studies

Published at May 31, 2023 ·  12 min read

In a recent post we have seen that we can use Principal Component Analyses (PCA) to elucidate the ‘genotype by environment’ relationship (see this post). Whenever the starting point for PCA is the doubly-centered (centered by rows and columns) matrix of yields across environments, we talk about AMMI analysis, which is often used to get insight into the stability of genotype yields across environments. By changing the starting matrix, we can obtain a different perspective and put focus on the definition of macroenvironments and on the selection of winning genotypes....

AMMI analyses for multi-environment studies

Published at May 26, 2023 ·  19 min read

Again into a subject that is rather important for most agronomists, i.e. the selection of crop varieties. All farmers are perfectly aware that crop performances are affected both by the genotype and by the environment. These two effects are not purely additive and they often show a significant interaction. By this word, we mean that a genotype can give particularly good/bad performances in some specific environmental situations, which we may not expect, considering its average behaviour in other environmental conditions....

Repeated measures with perennial crops

Published at March 30, 2023 ·  8 min read

In this post, I want to discuss a concept that is often mistaken by some of my collegues. With all crops, we are used to repeating experiments across years to obtain multi-year data; the structure of the resulting dataset is always the same and it is exemplified in the box below, that refers to a multi-year genotype experiment with winter wheat. rm(list = ls()) library(tidyverse) library(nlme) library(emmeans) filePath <- "https://www....

Subsampling in field experiments

Published at March 29, 2023 ·  11 min read

Subsampling is very common in field experiments in agriculture. It happens when we collect several random samples from each plot and we submit them to some sort of measurement process. Some examples? Let’s imagine that we have randomised field experiments with three replicates and, either,: we collect the whole grain yield in each plot, select four subsamples and measure, in each subsample, the oil content or some other relevant chemical property, or we collect, from each plot, four plants and measure their heights, or we collect a representative soil sample from each plot and perform chemical analyses in triplicate....

Fitting threshold models to seed germination data

Published at March 13, 2023 ·  19 min read

In previous posts we have shown that we can use time-to-event curves to describe the germination pattern of a seed population (see here). We have also shown that these curves can be modified to include the effects of external/internal factors/covariates, such as the genotype, the species, the humidity content and temperature in the substrate (see here and here). These modified time-to-event curves can be fitted in ‘one-step’, i.e., we start from the germination data with the appropriate shape (see here), fit the model and retrieve the estimates of model parameters ( go to here for an example )....

Fitting hydro-thermal-time-models to seed germination data

Published at February 10, 2023 ·  15 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Germination assay This dataset was obtained from previously published work (Mesgaran et al., 2017) with Hordeum spontaneum [C. Koch] Thell. The germination assay was conducted using four replicates of 20 seeds tested at six different water potential levels (0, −0....

Fitting thermal-time-models to seed germination data

Published at February 10, 2023 ·  7 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. A motivating examples In recent times, we wanted to model the effect of temperature on seed germination for Hordeum vulgare and we made an assay with three replicated Petri dishes (50 seeds each) at 9 constant temperature levels (1, 3, 7, 10, 15, 20, 25, 30, 35, 40 °C)....