A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance¿covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities

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

Methodology to Evaluate Forage Legumes for Oversowing Grasslands in the Basaltic Region of Uruguay

A methodology used to evaluate around 300 temperate and subtropical forage legumes for oversowing the native grasslands of the Basaltic Region of Uruguay is presented in a four-year plan using the minimum amount of seeds per accession and resources. Row-column experimental designs are used to reduce the error variance existing due to the large soil heterogeneity intrinsic to the Region. The ability of the species to grow and reproduce was measured and adjusted least square means were estimated to rank them. Cluster analysis was also useful to group species with similar behaviour overall traits. Preliminary results for the temperate species showed that the methodology is useful for ranking and grouping a large number of forage legumes according to their overall trait performance

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

Stacking tolerance to drought and resistance to a parasitic weed in tropical hybrid maize for enhancing resilience to stress combinations

Open Access JournalMaize is a food security crop cultivated in the African savannas that are vulnerable to the occurrence of drought stress and Striga hermonthica infestation. The co-occurrence of these stresses can severely damage crop growth and productivity of maize. Until recently, maize breeding in International Institute of Tropical Agriculture (IITA) has focused on the development of either drought tolerant or S. hermonthica resistant germplasm using independent screening protocols. The present study was therefore conducted to examine the extent to which maize hybrids simultaneously expressing resistance to S. hermonthica and tolerance to drought (DTSTR) could be developed through sequential selection of parental lines using the two screening protocols. Regional trials involving 77 DTSTR and 22 commercial benchmark hybrids (STR and non-DTSTR) were then conducted under Striga-infested and non-infested conditions, managed drought stress and fully irrigated conditions as well as in multiple rainfed environments for 5 years. The observed yield reductions of 61% under managed drought stress and 23% under Striga-infestation created desirable stress levels leading to the detection of significant differences in grain yield among hybrids at individual stress and non-stress conditions. On average, the DTSTR hybrids out-yielded the STR and non-DTSTR commercial hybrids by 13–19% under managed drought stress and fully irrigated conditions and by −4 to 70% under Striga-infested and non-infested conditions. Among the DTSTR hybrids included in the regional trials, 33 were high yielders with better adaptability across environments under all stressful and non-stressful testing conditions. Twenty-four of the 33 DTSTR hybrids also yielded well across diverse rainfed environments. The genetic correlations of grain yield under managed drought stress with yield under Striga-infestation and multiple rainfed environments were 0.51 and 0.57, respectively. Also, a genetic correlation between yields under Striga-infestation with that recorded in multiple rainfed environments was 0.58. These results suggest that the sequential selection scheme offers an opportunity to accumulate desirable stress-related traits in parents contributing to superior agronomic performance in hybrids across stressful and diverse rainfed field environments that are commonly encountered in the tropical savannas of Africa

Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models—six non-G × E and three G × E models—on three basic cross-validation (CV) schemes— in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)— in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave-one environment out (CV0-Env); (ii) leave one site out (CV0- Site); and (iii) leave 1 yr out (CV0-Year). The reaction norm models with G × E effects showed higher PA than the non-G × E models. Among the CV schemes, CV2 and CV0-Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits— grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)—, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains

Genetic gains in potato breeding as measured by field testing of cultivars released during the last 200 years in the Nordic Region of Europe

Genetic gains (Delta(G)) are determined by the breeders' equation Delta(G) = [(ck sigma(2)(G))/(y sigma(P))], where c, k and y are the parental control, a function of the selection intensity and number of years to complete one selection cycle, respectively, while sigma(2)(G) and are sigma(P) the genetic variance and the square root of the phenotypic variance. Plant breeding programs should deliver above 1% of annual genetic gains after testing and selection. The aim of this research was to estimate genetic gains in potato breeding after testing of cultivars released in western Europe in the last 200 years under high yield potential, and stress-prone environments affected by a pest (late blight) or daylength. The annual genetic gains for tuber yield and flesh's starch content for potato breeding in Europe were about 0.3 and -0.1%, respectively, thus telling that the realized genetic gains of foreign cultivars for both traits are small or negative, respectively, in the Nordic testing sites. The national annual productivity gains in potato grown in Sweden were on average 0.7% in the last 60 years while the genetic gains for tuber yield considering only the table cultivars released after the 2nd World War were about 0.36%, thus showing that breeding contributed just above 1/2 of it. Furthermore, genetic gains for breeding low reducing sugars in the tuber flesh, and high host plant resistance to late blight were small (<0.2% per year). These results highlight that genetic gains are small when testing bred germplasm outside their target population of environments

TESTS AND ESTIMATORS OF MULTIPLICATIVE MODELS FOR VARIETY TRIALS

Some recently obtained results on cross validation, hypothesis test and estimation procedures for multiplicative models applied to multi-site crop variety trials are presented. The PRESS statistic is more sensitive to overfitting and choice of model form than data-splitting cross-validation. Because of their extreme liberality, Gollob F-tests should not be used to test multiplicative terms. FGH tests effectively control Type I error, but are conservative for tests of terms for which the previous term is small. Simulation tests have greater power than FGH tests, but still effectively control Type I error rates. Simulation results and cross validation in two examples suggest that BLUP style shrinkage estimators of multiplicative terms produce fitted models with predictive value at least as good as the best truncated models and would eliminate the need for cross validation as a criterion for model choice. Shrinkage estimators of multiplicative models were better than BLUPs computed under the assumption of random unpatterened interaction in one example and were at least as good in the second example. Both were much better than empirical cell means in both examples. It is suggested that variety performance estimates derived from shrinkage estimators of multiplicative models should replace empirical cell means routinely reported in experiment station crop variety trial bulletins