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Understanding trait–trait coordination is essential for successful plant breeding and crop modeling. Notably, plant size drives variation in morphological, physiological, and performance-related traits, as described by allometric laws in ecology. Yet, as allometric relationships have been limitedly studied in crops, how they influence and possibly limit crop performance remains unknown. Here, we review how an allometry perspective on crops gains insights into the phenotypic evolution during crop domestication, the breeding of varieties adapted to novel conditions, and the prediction of crop yields. As allometry is an active field of research, modeling and manipulating crop allometric relationships can help to develop more resilient and productive agricultural systems to face future challenges.

How could we know if agricultural development interventions make contributions to sustainable development goals (SDGs)? Genetically engineered (GE) crops are celebrated as a class of technological interventions that can realize multiple SDGs. But recent studies have revealed the gap between GE crop program goals and the approaches used to assess their impacts. Using four comprehensive reviews of GE crop socio-economic impacts, we identify common shortcomings across three themes: (a) scope, (b) approaches and (c) heterogeneity. We find that the evaluation sciences literature offers alternative assessment approaches that can enable evaluators to better assess impacts, and inform learning and decision-making. We recommend the use of methods that enable evaluations to look beyond the agronomic and productive effects of individual traits to understand wider socio-economic effects.

Understanding crop domestication provides a basis for ongoing genetic improvement of crops, especially in the utilization of wild crop relatives as a source of new variation and may guide the domestication of new crops. The Asia Pacific region is home to most of the world's human population and is a region in which many important crops were domesticated. Here we review the domestication of banana, citrus, coconut, macadamia, mango, millet, mungbean, rice, sugarcane and taro in the Asia Pacific region. These examples illustrate the importance of this region in the development of agriculture. The challenges of conservation of the genetic resources for these crops are exacerbated by the large human population and rapid economic development in the region. Advances in genetic technologies provide an opportunity for accelerated genetic improvement of these crops and the domestication of new crops.

The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment, and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems. In this study, we propose a set of simple and generic formalisms (i.e. the structure and mathematical representation necessary for designing a model) for simulating key interactions in bi-specific intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Soybean-Sunflower, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for grain yield). This is the first time a complete set of formalisms has been developed and published for simulating bi-specific intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing in silico

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.

Description of the subject. Weed pressure is a main biotic constraint in tropical agriculture. Cover crop mixtures have increased in popularity to limit weed growth through competition for resources, but the relationship between cover crop diversity and weed suppression is still under debate. Objectives. This study aimed to assess the impact of increasing cover crop diversity (one to four species) on weed control during two growing seasons (tropical summer and winter) in Reunion Island. Method. Weed control was expressed regarding ground cover by weeds and weed aboveground dry mass in the mixtures during four months of growth and its response to cover crop traits was tested using structural equation models. Results. While cover crops reduced weed ground cover and dry mass by 60% and 68% on average in summer and winter, respectively, a higher number of cover crop species within a mixture did not increase mean weed control. Nonetheless, weed control was influenced by the mixture composition and improved when including Guizotia abyssinica. Additionally, cover crop traits explaining weed control differed between growing seasons. In summer, weed control was mainly explained by the final cover crop aboveground biomass and leaf area (depletion strategy). In contrast, weed control was mainly explained by the cover crop rate of increase in ground cover (obstruction strategy) in winter. Conclusions. Using traits to characterize cover crop mixture enables us to identify mixtures of species and traits adapted to different growing conditions. Our study suggests that particular attention on species identity rather than diversity should be paid in mixture to improve weed control in tropical conditions.

Quinoa (Chenopodium quinoa Willd.) is an Andean herbaceous crop that has attracted increasing interest in recent years thanks to its ecophysiological behavior and the nutritional characteristics of its seeds. The quinoa boom followed the celebration of the International Year of Quinoa in 2013 by the United Nations (FAO), when numerous initiatives were implemented to spread the positive characteristics that make quinoa a suitable crop with which to fight world hunger. In this Special Issue, we wanted to summarize the state of the art and the main research activities that are currently underway in different parts of the world.

CONTEXT: Small farms rely on a range of nature's contributions to people (NCPs) provided by crop diversity, covering both material and immaterial dimensions that are crucial for livelihoods and well-being. The maintenance of these NCPs over time, despite perturbations, is a key component of small farms' resilience. However, the processes involved in farmers accessing the different NCPs provided by crops are largely unknown. Such knowledge would be instrumental for evaluating the vulnerability or resilience of farmers to potential disruptions that affect these distribution channels. OBJECTIVE: In this study, we analyzed how the seed provisioning networks used by farmers to access crops relate to the different NCPs they receive from these crops, through a case study in Sahelian Senegal. METHODS: Field surveys were conducted with 85 farmers, half men and half women, from two villages. The surveys documented which varieties of three important staple crop species (pearl millet, cowpea, peanut) farmers grew. Farmers were asked to cite their motivations for cultivating each variety as a proxy for NCPs, and to explain from where they obtained the seeds of each variety of these three species. We mobilized recent developments in Social-Ecological Network research, representing the relationships between social entities (i.e., farmers and seed sources), ecological entities (i.e., crops), and NCPs (i.e., motivations) as networks. We applied a block model clustering approach to analyze these relationships by testing if particular seed sources were associated with particular motivations, and if differences existed between men and women. We also analyzed households' profiles according to the motivations they cited and the seed sources they were connected to. RESULTS AND CONCLUSIONS: We found that some crops contributions were related to different seed sources, for instance crops associated to food provision were sourced through markets, peers, and legacy, while other contributions

The expansion of commercial agriculture is one of the primary drivers of livelihood and land-use changes in the world. Globalisation and other factors have intensified this expansion to the point where booms in single cash crops overtake entire regions before going bust, a pattern that is particularly pervasive in resource frontiers. Using case studies across the Mekong Region, a place which serves as a harbinger for crop booms globally, we propose a new analytical framework for understanding and governing crop booms. We combine multiple theoretical approaches to study crop booms and draw on insights from case study work conducted across temporal and spatial scales. The framework consists of three components: 1) the nested nature of crop boom-bust trajectories, 2) the cyclical spatial and temporal patterns of crop booms, and 3) the variegated pathways and impacts of agrarian change. The framework presents new insights into the processes of agricultural intensification in frontier spaces. As such, it facilitates a better understanding of the drivers, characteristics and impacts of crop booms for researchers and decision-makers alike with the intention of supporting efforts to develop more sustainable pathways in the region and beyond.

Quinoa is known as a super food due to its extraordinary nutritional qualities and has the potential to ensure future global food and nutritional security. As a model plant with halophytic behavior, quinoa has potential to meet the challenges of climate change and salinization due to its capabilities for survival in harsh climatic conditions. The quinoa crop has received worldwide attention due to its adoption and production expanded in countries out of the native Andean region. Quinoa was introduced to Pakistan in 2009 and it is still a new crop in Pakistan. The first quinoa variety was registered in 2019, then afterward, its cultivation started on a larger scale. Weed pressure, terminal heat stress, stem lodging, bold grain size, and an unstructured market are the major challenges in the production and promotion of the crop. The potential of superior features of quinoa has not been fully explored and utilized. Hence, there is a need to acquire more diverse quinoa germplasm and to establish a strong breeding program to develop new lines with higher productivity and improved crop features for the Pakistan market. Mechanized production, processing practices, and a structured market are needed for further scaling of quinoa production in Pakistan. To achieve these objectives, there is a dire need to create an enabling environment for quinoa production and promotion through the involvement of policymakers, research institutions, farmers associations, and the private sector.

Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated data sets computed from a set of more than 360 000 random TOY parameter values and random climate values. These data sets were used for training and for validation. For this purpose, we chose VoxNet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for VoxNet inputs, outputs and training. We were able to teach the network to predict the value of environment data well (mean error < 2 %), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5 % for all parameters), and for any environmental growing conditions (mean error < 20 %).

This book focuses on three important elements in the development of cereal and legume crops in semi-arid West Africa. The first section illustrates the socioeconomic factors that affect the food system for these crops and contains an overview of crop production and consumption in the region. Then, important inputs that affect system productivity are presented: preferences for new seed varieties, yield response to fertilizer, counterfeit herbicides, climate information, and the way farmers develop expectations about the weather events that shape cropping outcomes. The final chapter of section one is dedicated to understanding urban consumer preferences for processed food products derived from pearl millet. The second section addresses the state of the art of phenotyping and the modeling of crop adaptation to dryland farming systems. Chapters in section two focus on the regional network for phenotyping, high throughput phenotyping in field situations, root architectures, and crop ideotypes. In addition, empirical evidence is presented on root and soil interactions and the effectiveness of unmanned aerial vehicles (UAVs to assess sorghum physiology. Finally, the third section investigates the genetic diversity and improvement of dryland crops. Chapters in this section focus on biodiversity and agricultural system sustainability, orphan crops such as fonio, and the utilization of a regional germplasm collection in the improvement of cowpea, groundnut, pearl millet, and sorghum.

The OMICAS alliance is part of the Colombian government's Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program's kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS' R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Process-based crop models are popular tools to analyze and simulate the response of agricultural systems to weather, agronomic, or genetic factors. They are often developed in modeling platforms to ensure their future extension and to couple different crop models with a soil model and a crop management event scheduler. The intercomparison and improvement of crop simulation models is difficult due to the lack of efficient methods for exchanging biophysical processes between modeling platforms. We developed Crop2ML, a modeling framework that enables the description and the assembly of crop model components independently of the formalism of modeling platforms and the exchange of components between platforms. Crop2ML is based on a declarative architecture of modular model representation to describe the biophysical processes and their transformation to model components that conform to crop modeling platforms. Here, we present Crop2ML framework and describe the mechanisms of import and export between Crop2ML and modeling platforms.

As plants cannot relocate, they require effective root systems for water and nutrient uptake. Root development plasticity enables plants to adapt to different environmental conditions. Research on improvements in crop root systems is limited in comparison with that in shoots as the former are difficult to image. Breeding more effective root systems is proposed as the “second green revolution”. There are several recent publications on root system architecture (RSA), but the methods used to analyze the RSA have not been standardized. Here, we introduce traditional and current root-imaging methods and discuss root structure phenotyping. Some important root structures have not been standardized as roots are easily affected by rhizosphere conditions and exhibit greater plasticity than shoots; moreover, root morphology significantly varies even in the same genotype. For these reasons, it is difficult to define the ideal root systems for breeding. In this review, we introduce several types of software to analyze roots and identify important root parameters by modeling to simplify the root system characterization. These parameters can be extracted from photographs captured in the field. This modeling approach is applicable to various legacy root data stored in old or unpublished formats. Standardization of RSA data could help estimate root ideotypes.

Cowpea (Vigna unguiculata (L.)) is an important crop for food security in Senegal; therefore, understanding the genetic diversity of local germplasm is relevant for crop improvement and genetic maintenance in the era of climate change. For this purpose, 15 microsatellite markers were used to estimate the genetic diversity of Senegalese cowpea germplasm, including 671 accessions grown in eight regions and 66 wild relatives and intermediate forms (weedy). For the cultivated, the main expected heterozygosity (mHe) ranged between 0.317 (Fatick) and 0.439 (South). A narrow genetic variation between accessions from the different regions was observed with genetic similarity ranging from 0.861 to 0.965 and genetic differentiation indices (Fst) between 0.018 and 0.100. The accessions from southern Senegal (Kédougou, Sédhiou, and Kolda regions) are more diverse than the others. However, the accessions from the North (Saint-Louis) are genetically different from other regions. The diversity analysis in wild relatives from Senegal, which had never been performed before, revealed that the wild/weedy forms remain more diverse than the cultivated with genetic diversity values (He) of 0.389 and 0.480, respectively. STRUCTURE software divided the Senegalese germplasm into five subpopulations. Three of them (i, ii, and iii) included only cultivated accessions from several regions, one (v) mainly from Saint-Louis, and one (iv) the wild/weedy with some cultivated accessions. Our results support the hypothesis that Vigna unguiculata var. spontanea is the wild progenitor of cowpea. The accessions from the South, the northern recession accessions, and the wild/weedy could serve as sources of new genes for the genetic improvement of cowpea in Senegal.

The narrow genetics of most crops is a fundamental vulnerability to food security. This makes wild crop relatives a strategic resource of genetic diversity that can be used for crop improvement and adaptation to new agricultural challenges. Here, we uncover the contribution of one wild species accession, Arachis cardenasii GKP 10017, to the peanut crop (Arachis hypogaea) that was initiated by complex hybridizations in the 1960s and propagated by international seed exchange. However, until this study, the global scale of the dispersal of genetic contributions from this wild accession had been obscured by the multiple germplasm transfers, breeding cycles, and unrecorded genetic mixing between lineages that had occurred over the years. By genetic analysis and pedigree research, we identified A. cardenasii–enhanced, disease-resistant cultivars in Africa, Asia, Oceania, and the Americas. These cultivars provide widespread improved food security and environmental and economic benefits. This study emphasizes the importance of wild species and collaborative networks of international expertise for crop improvement. However, it also highlights the consequences of the implementation of a patchwork of restrictive national laws and sea changes in attitudes regarding germplasm that followed in the wake of the Convention on Biological Diversity. Today, the botanical collections and multiple seed exchanges which enable benefits such as those revealed by this study are drastically reduced. The research reported here underscores the vital importance of ready access to germplasm in ensuring long-term world food security.

Premise: Weed removal in agriculture is typically achieved using herbicides. The use of autonomous robots to reduce weeds is a promising alternative solution, although their implementation requires the precise detection and identification of crops and weeds to allow an efficient action. Methods: We trained and evaluated an instance segmentation convolutional neural network aimed at segmenting and identifying each plant specimen visible in images produced by agricultural robots. The resulting data set comprised field images on which the outlines of 2489 specimens from two crop species and four weed species were manually drawn. We adjusted the hyperparameters of a mask region‐based convolutional neural network (R‐CNN) to this specific task and evaluated the resulting trained model. Results: The probability of detection using the model was quite good but varied significantly depending on the species and size of the plants. In practice, between 10% and 60% of weeds could be removed without too high of a risk of confusion with crop plants. Furthermore, we show that the segmentation of each plant enabled the determination of precise action points such as the barycenter of the plant surface. Discussion: Instance segmentation opens many possibilities for optimized weed removal actions. Weed electrification, for instance, could benefit from the targeted adjustment of the voltage, frequency, and location of the electrode to the plant. The results of this work will enable the evaluation of this type of weeding approach in the coming months.

Global environmental changes strongly impact wild and domesticated species biology and their associated ecosystem services. For crops, global warming has led to significant changes in terms of phenology and/or yield. To respond to the agricultural challenges of this century, there is a strong need for harnessing the genetic variability of crops and adapting them to new conditions. Gene flow, from either the same species or a different species, may be an immediate primary source to widen genetic diversity and adaptions to various environments. When the incorporation of a foreign variant leads to an increase of the fitness of the recipient pool, it is referred to as “adaptive introgression”. Crop species are excellent case studies of this phenomenon since their genetic variability has been considerably reduced over space and time but most of them continue exchanging genetic material with their wild relatives. In this paper, we review studies of adaptive introgression, presenting methodological approaches and challenges to detecting it. We pay particular attention to the potential of this evolutionary mechanism for the adaptation of crops. Furthermore, we discuss the importance of farmers' knowledge and practices in shaping wild-to-crop gene flow. Finally, we argue that screening the wild introgression already existing in the cultivated gene pool may be an effective strategy for uncovering wild diversity relevant for crop adaptation to current environmental changes and for informing new breeding directions.

The fall armyworm, Spodoptera frugiperda (J. E. Smith)(Lepidoptera: Noctuidae), is an important agricultural pest of the Western Hemisphere noted for its broad host range, long distance flight capabilities, and a propensity to develop resistance to pesticides that includes a subset of those used in genetically modified corn varieties. These characteristics exacerbate the threat fall armyworm poses to agriculture, with the potential that a resistance trait arising in one geographical location could rapidly disseminate throughout the hemisphere. A region of particular concern is the Caribbean, where a line of islands that extends from Florida to Venezuela provides a potential migratory pathway between populations from North and South America that could allow for consistent and substantial genetic interactions. In this study, surveys of populations from Peru, Bolivia, Paraguay, and Trinidad & Tobago expand on previous work in South America that indicates a generally homogeneous population with respect to haplotype markers. This population differs from that found in most of the Lesser Antilles where a combination of genetic and meteorological observations is described that indicate fall armyworm migration from Puerto Rico to as far south as Barbados, but does not support significant incursion into Trinidad & Tobago and South America. Air transport projections demonstrate that the wind patterns in the Caribbean region are not conducive to consistent flight along the north-south orientation of the Lesser Antilles, supporting the conclusion that such migration is minor and sporadic, providing few opportunities for genetic exchanges. The implications of these findings on the dissemination of deleterious traits between the two Western Hemisphere continents are discussed.

The structure of pathogen populations is an important driver of epidemics affecting crops and natural plant communities. Comparing the composition of two pathogen populations consisting of assemblages of genotypes or phenotypes is a crucial, recurrent question encountered in many studies in plant disease epidemiology. Determining whether there is a significant difference between two sets of proportions is also a generic question for numerous biological fields. When samples are small and data are sparse, it is not straightforward to provide an accurate answer to this simple question because routine statistical tests may not be exactly calibrated. To tackle this issue, we built a computationally intensive testing procedure, the generalized Monte Carlo plug-in test with calibration test, which is implemented in an R package available at https://doi.org/10.5281/zenodo.635791. A simulation study was carried out to assess the performance of the proposed methodology and to make a comparison with standard statistical tests. This study allows us to give advice on how to apply the proposed method, depending on the sample sizes. The proposed methodology was then applied to real datasets and the results of the analyses were discussed from an epidemiological perspective. The applications to real data sets deal with three topics in plant pathology: the reproduction of Magnaporthe oryzae, the spatial structure of Pseudomonas syringae, and the temporal recurrence of Puccinia triticina.

Dans les zones oasiennes, les ressources en eau de surface deviennent de plus en plus rares et les ressources en eau souterraines sont souvent surexploitées et de mauvaise qualité avec une salinité élevée. L'économie de l'eau et sa préservation est devenue par conséquent un impératif pour la durabilité des oasis. Si l'irrigation localisée est actuellement préconisée au Maroc pour l'économie de l'eau, son utilisation dans les régions subdésertiques ne permet pas de réduire les taux élevés des pertes par évaporation. Une alternative à ce système serait l'utilisation de la technique d'irrigation souterraine au moyen d'émetteurs débitant l'eau à des taux généralement similaire à l'irrigation goutte à goutte de surface. L'irrigation souterraine étant une technique très récemment introduite au Maroc, une meilleure compréhension dans les conditions locales du processus d'infiltration, et son impact sur la croissance d'une plante comme le palmier dattier s'avère nécessaire. Cette étude vise à contribuer à l'amélioration de l'efficience de l'utilisation de l'eau en testant la performance du système d'irrigation souterraine dans un contexte où l'eau est une ressource limitée. L'objectif de ce travail de recherche est l'évaluation des performances en conditions arides d'un système d'irrigation au goutte à goutte enterré sur jeunes palmiers dattiers en comparaison avec le goutte à goutte de surface. En préambule à ce travail, on s'attachera à déterminer une méthode adaptée à la mesure du débit d'un goutteur enterré. Un essai expérimental visant à caractériser les performances respectives du goutte à goutte de surface et du goutte à goutte enterré sur de jeunes palmiers dattiers a été mis en place chez un agriculteur de la région d'Erfoud (Province d'Errachidia, Sud-Est du Maroc). La mesure du débit pour évaluer l'uniformité de l'application a été faite selon deux méthodes: une mesure du débit des goutteurs en ''hors sol'' et une autre en présence du sol. Cette dernière méthode

In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013–2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis.

Crop diversification is one way of improving the profitability of small-scale irrigation schemes. The 25 de Setembro scheme is an ideal site to analyze diversification, as it is influenced by the markets in Maputo and South Africa. This study uses information gathered from observations, discussions with irrigators and an irrigator survey. Results identified seven irrigator types with different crop diversification strategies predominantly influenced by resource constraints. Most irrigators produce traditional crops, although there are opportunities for growing crops that are more profitable. Improved extension services, to identify cropping strategies that better align with market demand, would improve profitability.

The genus Theobroma comprises several trees species native to the Amazon. Theobroma cacao L. plays a key economic role mainly in the chocolate industry. Both cultivated and wild forms are described within the genus. Variations in genome size and chromosome number have been used for prediction purposes including the frequency of interspecific hybridization or inference about evolutionary relationships. In this study, the nuclear DNA content, karyotype and genetic diversity using functional microsatellites (EST-SSR) of seven Theobroma species were characterized. The nuclear content of DNA for all analyzed Theobroma species was 1C = ~ 0.46 pg. These species presented 2n = 20 with small chromosomes and only one pair of terminal heterochromatic bands positively stained (CMA+/DAPI− bands). The small size of Theobroma ssp. genomes was equivalent to other Byttnerioideae species, suggesting that the basal lineage of Malvaceae have smaller genomes and that there was an expansion of 2C values in the more specialized family clades. A set of 20 EST-SSR primers were characterized for related species of Theobroma, in which 12 loci were polymorphic. The polymorphism information content (PIC) ranged from 0.23 to 0.65, indicating a high level of information per locus. Combined results of flow cytometry, cytogenetic data and EST-SSRs markers will contribute to better describe the species and infer about the evolutionary relationships among Theobroma species. In addition, the importance of a core collection for conservation purposes is highlighted.

The fall armyworm, Spodoptera frugiperda (J. E. Smith)(Lepidoptera: Noctuidae), is an important agricultural pest of the Western Hemisphere noted for its broad host range, long distance flight capabilities, and a propensity to develop resistance to pesticides that includes a subset of those used in genetically modified corn varieties. These characteristics exacerbate the threat fall armyworm poses to agriculture, with the potential that a resistance trait arising in one geographical location could rapidly disseminate throughout the hemisphere. A region of particular concern is the Caribbean, where a line of islands that extends from Florida to Venezuela provides a potential migratory pathway between populations from North and South America that could allow for consistent and substantial genetic interactions. In this study, surveys of populations from Peru, Bolivia, Paraguay, and Trinidad & Tobago expand on previous work in South America that indicates a generally homogeneous population with respect to haplotype markers. This population differs from that found in most of the Lesser Antilles where a combination of genetic and meteorological observations is described that indicate fall armyworm migration from Puerto Rico to as far south as Barbados, but does not support significant incursion into Trinidad & Tobago and South America. Air transport projections demonstrate that the wind patterns in the Caribbean region are not conducive to consistent flight along the north-south orientation of the Lesser Antilles, supporting the conclusion that such migration is minor and sporadic, providing few opportunities for genetic exchanges. The implications of these findings on the dissemination of deleterious traits between the two Western Hemisphere continents are discussed.