I.4 Screening Experimental Designs for Quantitative Trait Loci, Association Mapping, Genotype-by Environment Interaction, and Other Investigations

Crop breeding programs using conventional approaches, as well as new biotechnological tools, rely heavily on data resulting from the evaluation of genotypes in different environmental conditions (agronomic practices, locations, and years). Statistical methods used for designing field and laboratory trials and for analyzing the data originating from those trials need to be accurate and efficient. The statistical analysis of multi-environment trails (MET) is useful for assessing genotype × environment interaction (GEI), mapping quantitative trait loci (QTLs), and studying QTL × environment interaction (QEI). Large populations are required for scientific study of QEI, and for determining the association between molecular markers and quantitative trait variability. Therefore, appropriate control of local variability through efficient experimental design is of key importance. In this chapter we present and explain several classes of augmented designs useful for achieving control of variability and assessing genotype effects in a practical and efficient manner. A popular procedure for unreplicated designs is the one known as “systematically spaced checks.” Augmented designs contain “c” check or standard treatments replicated “r” times, and “n” new treatments or genotypes included once (usually) in the experiment

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers

A traditional floodplain fishery of the lower Amazon River, Brazil

This paper describes fishing activities of households in four communities located in a floodplain lake system of the lower Amazon river. An average of 42 households were interviewed about their fishing activity on a monthly basis. The fishery is a typical multi-gear, multi-specific artisanal fishery. Approximately ten types of fishing gear are utilized, of which the three main types of gillnets account for 51% of the total catch. The catch per trip averaged 15 kg, for an annual total of 2,295 kg per household. Some 40 species or groups of species are caught, although four species account for 50% of the total. There is a strong seasonal pattern to the fishery, with catch per trip and catch per unit effort (CPUE) highest in the low water season (September-November). While there are marked differences between subsistence and commercially oriented fishing strategies, these differences are more in degree than in type, since fishers use the same types of gear and most fishers regularly sell part of their catch

Two-dimensional mapping of micro-hardness increase on surface treated steel determined by photothermal deflection microscopy

An optical noncontact technique is presented that provides a two-dimensional map of the hardness of treated steel at the micrometer level. The photodeflection technique for determining the thermal diffusivity is shown to be a useful and rapid way to determine the hardness increase profile in two dimensions with only minor preparation of the sample (flat polish). This is possible due to the strong correlation found for this type of material between the inverse of the diffusivity and the hardness increment after treatment. The diffusivity retrieval is performed by a single measurement of the phase delay between the pump beam and the photodeflection signal thus allowing a rapid scanning of the surface. The surface scans of the hardness performed with this technique showed that anomalous regions can be identified that direct optical or scanning electron microscopy observation do not reveal.Fil: Crossa Archiopoli, Ulises. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Mingolo, Nélida. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Martinez, Oscar Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Belgrano. Facultad de Ciencias Exactas y Naturales; Argentin

Multivariate Statistical Machine Learning Methods for Genomic Prediction

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool