New proposals to strengthen the boom of agriculture in Peru: massive use of genetic resources and development of modern breeding programs

Peru is a place with abundant biological resources that should be employed for the benefit of society in general. However, to date, the use of Peruvian plant genetic resources was not fully exploited for the development of improved crops. This work was mostly conducted by the international private sector. The Climate Change Laboratory at Instituto Nacional de Innovación Agraria, and other laboratories at Universidad Nacional José Faustino Sánchez Carrión and Universidad Nacional Agraria La Molina together with other research programs of other institutions seek to promote the massive and sustainable use of plant genetic resources maintained in germplasm banks. It is planned to make use of modern molecular and morphological techniques. Moreover, infrastructure and human resources are being improved. As a result, we will be able to maintain the growth of the agricultural activity in Peru in terms of space and time.

Methodological proposal for the characterization of accessions in Germplasm Banks using Generalized Procrustes Analysis applied to incomplete but connected trials

Characterization of plant material conserved in germplasm banks allows the study and analysis of the genetic variability within a collection. When germplasm banks have a large number of accessions, field evaluation should be performed using assays with manageable accession subsets. Common checks connecting the different assays are required to compare these accession subsets. In this study, the Generalized Procrustes Analysis was proposed as a basis for obtaining a factorial plane where all individuals are projected. This technique is applied to genotypes common to all assays, iteratively generating scale factors and rotation matrices. Accessions only belonging to a given assay are considered supplementary elements. This proposal was illustrated using datasets of 54 maize accessions from the Pergamino Active Germplasm Bank of the Experimental Station at the Instituto Nacional de Tecnología Agropecuaria (INTA) in Argentina. The proposal achieved highly satisfactory results. Highlights: In field evaluation of large germplasm collections, the material must be divided into manageable experimental trials, in which different accession subsets are evaluated in different environments. A new algorithm based on Generalized Procrustes Analysis (GPA) allowed to find the consensus of several configurations of individuals connected by common checks. The characterization data analysis strategy was illustrated using a set of accessions from the Argentine Maize Germplasm Bank. The new proposal stands as a useful tool for evaluate germplasm collections, providing good results with easy implementation and considering the multivariate structure of the data set.

Ex situ PLANT GERMPLASM CONSERVATION REVISED AT THE LIGHT OF MECHANISMS AND METHODS OF GENETICS

Plant genetic resources for food and agriculture are ex situ conserved in germplasm banks as samples (accessions) of natural or naturalized populations, either as the originally sampled propagules (mainly seeds) or their multiplications. The premises underlying ex situ conservation are that (a) it is the safest and cheapest alternative for germplasm preservation for future generations and (b) accessions are representative of the genetic diversity encountered in nature. In the past decades, ideas, alternatives and considerations have been put forward on the topic, and protocols have been devised for plant germplasm sampling, conservation and multiplication. However, limitations in the management efficiency of germplasm banks have been pointed out by international organizations. In our opinion, germplasm banks in general need to revise their functioning and management at the light of principles and methods of Genetics. To that end, it is necessary to consider the reproductive biology of higher plants -whose genetic consequences at both the individual plant and the population levels are not always either fully understood or taken into account in devising the protocols-, the genetic structures of wild and cultivated populations, and the course of the genetic material in the populations. In this paper, we discuss the three topics and provide an example of a national forage breeding program, from germplasm bank accessions as the germplasm of origin to the obtainment of commercial cultivars. Finally, we present a proposal as a base for discussion among curators, researchers and breeders. Key words: accessions, breeding, genetic resources, germplasm banks, population genetics

Ex situ PLANT GERMPLASM CONSERVATION REVISED AT THE LIGHT OF MECHANISMS AND METHODS OF GENETICS

Plant genetic resources for food and agriculture are ex situ conserved in germplasm banks as samples (accessions) of natural or naturalized populations, either as the originally sampled propagules (mainly seeds) or their multiplications. The premises underlying ex situ conservation are that (a) it is the safest and cheapest alternative for germplasm preservation for future generations and (b) accessions are representative of the genetic diversity encountered in nature. In the past decades, ideas, alternatives and considerations have been put forward on the topic, and protocols have been devised for plant germplasm sampling, conservation and multiplication. However, limitations in the management efficiency of germplasm banks have been pointed out by international organizations. In our opinion, germplasm banks in general need to revise their functioning and management at the light of principles and methods of Genetics. To that end, it is necessary to consider the reproductive biology of higher plants -whose genetic consequences at both the individual plant and the population levels are not always either fully understood or taken into account in devising the protocols-, the genetic structures of wild and cultivated populations, and the course of the genetic material in the populations. In this paper, we discuss the three topics and provide an example of a national forage breeding program, from germplasm bank accessions as the germplasm of origin to the obtainment of commercial cultivars. Finally, we present a proposal as a base for discussion among curators, researchers and breeders. Key words: accessions, breeding, genetic resources, germplasm banks, population genetics

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.

The impact of sample selection strategies on genetic diversity and representativeness in germplasm bank collections

Background Germplasm banks maintain collections representing the most comprehensive catalogue of native genetic diversity available for crop improvement. Users of germplasm banks are interested in a fixed number of samples representing as broadly as possible the diversity present in the wider collection. A relevant question is whether it is necessary to develop completely independent germplasm samples or it is possible to select nested sets from a pre-defined core set panel not from the whole collection. We used data from 15,384, maize landraces stored in the CIMMYT germplasm bank to study the impact on 8 diversity criteria and the sample representativeness of: (1) two core selection strategies, a statistical sampling (DM), or a numerical maximization method (CH); (2) selecting samples of varying sizes; and (3) selecting samples of different sizes independently of each other or in a nested manner. Results Sample sizes greater than 10% of the whole population size retained more than 75% of the polymorphic markers for all selection strategies and types of sample; lower sample sizes showed more variability (instability) among repetitions; the strongest effect of sample size was observed on the CH-independent combination. Independent and nested samples showed similar performance for all the criteria for the DM method, but there were differences between them for the CH method. The DM method achieved better approximations to the known values in the population than the CH method; 2-d multidimensional scaling plots of the collection and samples highlighted tendency of sample selection towards the extremes of diversity in the CH method, compared with sampling more representative of the overall genotypic distribution of diversity under the DM method. Conclusions The use of core subsets of size greater than or equal to 10% of the whole collection satisfied well the requirement of representativeness and diversity. Nested samples showed similar diversity and representativeness characteristics as independent samples offering a cost effective method of sample definition for germplasm banks. For most criteria assessed the DM method achieved better approximations to the known values in the whole population than the CH method, that is, it generated more statistically representative samples from collections.