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Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat ( Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome 1 , and the lack of genome-assembly data for multiple wheat lines 2 , 3 . Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses 4 , 5 . We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm1 6 , a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars. Comparison of multiple genome assemblies from wheat reveals extensive diversity that results from the complex breeding history of wheat and provides a basis for further potential improvements to this important food crop.

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size and used to construct a large standard database called Pest ID . This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

Plant immune responses mediated by the hormone jasmonoyl-L-isoleucine (JA-Ile) are metabolically costly and often linked to reduced growth. Although it is known that JA-Ile activates defense responses by triggering the degradation of JASMONATE ZIM DOMAIN (JAZ) transcriptional repressor proteins, expansion of the JAZ gene family in vascular plants has hampered efforts to understand how this hormone impacts growth and other physiological tasks over the course of ontogeny. Here, we combined mutations within the 13-member Arabidopsis JAZ gene family to investigate the effects of chronic JAZ deficiency on growth, defense, and reproductive output. A higher-order mutant (jaz decuple, jazD) defective in 10 JAZ genes (JAZ1–7, -9, -10, and -13) exhibited robust resistance to insect herbivores and fungal pathogens, which was accompanied by slow vegetative growth and poor reproductive performance. Metabolic phenotypes of jazD discerned from global transcript and protein profiling were indicative of elevated carbon partitioning to amino acid-, protein-, and endoplasmic reticulum body-based defenses controlled by the JA-Ile and ethylene branches of immunity. Resource allocation to a strong defense sink in jazD leaves was associated with increased respiration and hallmarks of carbon starvation but no overt changes in photosynthetic rate. Depletion of the remaining JAZ repressors in jazD further exaggerated growth stunting, nearly abolished seed production and, under extreme conditions, caused spreading necrotic lesions and tissue death. Our results demonstrate that JAZ proteins promote growth and reproductive success at least in part by preventing catastrophic metabolic effects of an unrestrained immune response.

The brown planthopper (BPH) and white-backed planthopper (WBPH) are the most destructive insect pests of rice, and they pose serious threats to rice production throughout Asia. Thus, there are urgent needs to identify resistance-conferring genes and to breed planthopper-resistant rice varieties. Here we report the map-based cloning and functional analysis of Bph6 , a gene that confers resistance to planthoppers in rice. Bph6 encodes a previously uncharacterized protein that localizes to exocysts and interacts with the exocyst subunit OsEXO70E1. Bph6 expression increases exocytosis and participates in cell wall maintenance and reinforcement. A coordinated cytokinin, salicylic acid and jasmonic acid signaling pathway is activated in Bph6 -carrying plants, which display broad resistance to all tested BPH biotypes and to WBPH without sacrificing yield, as these plants were found to maintain a high level of performance in a field that was heavily infested with BPH. Our results suggest that a superior resistance gene that evolved long ago in a region where planthoppers are found year round could be very valuable for controlling agricultural insect pests. The study reports map-based cloning and functional analysis of Bph6 , which is associated with resistance to planthoppers in rice. BPH6 localizes to the exocyst and interacts with OsEXO70E1, and suppression of OsExo70E1 expression decreases resistance in Bph6 -NIL plants.

Legumes are affected by biotic factors such as insects, molds, bacteria, and viruses. These plants can produce many different molecules in response to the attack of phytopathogens. Protease inhibitors (PIs) are proteins produced by legumes that inhibit the protease activity of phytopathogens. PIs are known to reduce nutrient availability, which diminishes pathogen growth and can lead to the death of the pathogen. PIs are classified according to the specificity of the mechanistic activity of the proteolytic enzymes, with serine and cysteine protease inhibitors being studied the most. Previous investigations have reported the efficacy of these highly stable proteins against diverse biotic factors and the concomitant protective effects in crops, representing a possible replacement of toxic agrochemicals that harm the environment.

This Analysis of data from 38 field studies identifies key factors affecting the durability of Bt toxin pyramids, and should inform future resistance management strategies. Transgenic crop pyramids producing two or more Bacillus thuringiensis (Bt) toxins that kill the same insect pest have been widely used to delay evolution of pest resistance. To assess the potential of pyramids to achieve this goal, we analyze data from 38 studies that report effects of ten Bt toxins used in transgenic crops against 15 insect pests. We find that compared with optimal low levels of insect survival, survival on currently used pyramids is often higher for both susceptible insects and insects resistant to one of the toxins in the pyramid. Furthermore, we find that cross-resistance and antagonism between toxins used in pyramids are common, and that these problems are associated with the similarity of the amino acid sequences of domains II and III of the toxins, respectively. This analysis should assist in future pyramid design and the development of sustainable resistance management strategies.

Combining four years of field data with computer modeling reveals that development of resistance to Bacillus thuringiensis insecticidal proteins (Bt) in cotton bollworm can be delayed by refuges of non-Bt host plants other than cotton, but that these so-called ‘natural refuges’ are not as effective as non-Bt cotton refuges. The ‘natural refuge strategy” for delaying insect resistance to transgenic cotton that produces insecticidal proteins from Bacillus thuringiensis (Bt) relies on refuges of host plants other than cotton that do not make Bt toxins. We tested this widely adopted strategy by comparing predictions from modeling with data from a four-year field study of cotton bollworm ( Helicoverpa armigera ) resistance to transgenic cotton producing Bt toxin Cry1Ac in six provinces of northern China. Bioassay data revealed that the percentage of resistant insects increased from 0.93% in 2010 to 5.5% in 2013. Modeling predicted that the percentage of resistant insects would exceed 98% in 2013 without natural refuges, but would increase to only 1.1% if natural refuges were as effective as non-Bt cotton refuges. Therefore, the results imply that natural refuges delayed resistance, but were not as effective as an equivalent area of non-Bt cotton refuges. The percentage of resistant insects with nonrecessive inheritance of resistance increased from 37% in 2010 to 84% in 2013. Switching to Bt cotton producing two or more toxins and integrating other control tactics could slow further increases in resistance.

Plants rely on complex blends of constitutive and dynamically produced specialized metabolites to mediate beneficial ecological interactions and protect against biotic attack. One such class of metabolites are terpenoids, a large and structurally diverse class of molecules shown to play significant defensive and developmental roles in numerous plant species. Despite this, terpenoids have only recently been recognized as significant contributors to pest resistance in maize (Zea mays), a globally important agricultural crop. The current review details recent advances in our understanding of biochemical structures, pathways and functional roles of maize terpenoids. Dependent upon the lines examined, maize can harbor more than 30 terpene synthases, underlying the inherent diversity of maize terpene defense systems. Part of this defensive arsenal is the inducible production of volatile bouquets that include monoterpenes, homoterpenes and sesquiterpenes, which often function in indirect defense by enabling the attraction of parasitoids and predators. More recently discovered are a subset of sesquiterpene and diterpene hydrocarbon olefins modified by cytochrome P450s to produce non-volatile end-products such kauralexins, zealexins, dolabralexins and ß-costic acid. These non-volatile terpenoid phytoalexins often provide effective defense against both microbial and insect pests via direct antimicrobial and anti-feedant activity. The diversity and promiscuity of maize terpene synthases, coupled with a variety of secondary modifications, results in elaborate defensive layers whose identities, regulation and precise functions are continuing to be elucidated.

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana , and the small tomato borer, Neoleucinodes elegantalis . We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

Even break for organic crops Declining species number (richness) harms ecosystems, and conservation efforts have largely focused on conserving or restoring particular rare species. However, greater disparity in species relative abundances (evenness) might also do ecological harm, which could only be reversed by altering the densities of many species at once. A new survey of organic and conventionally managed potato fields shows that species evenness is greater under organic management. Replicating these levels of evenness in a field trial shows that the evenness of natural enemies found in organic fields promotes pest control and increases crop biomass. In organic crops many beneficial species (that eat pest insects) are equally common, which in potatoes leads to fewer pests and larger plants. A survey of organic and conventional potato fields shows that species evenness is greater under organic management. Replicating these levels of evenness in a field trial shows that the evenness of natural enemies found in organic fields promotes pest control and increases crop biomass. This is independent of the identity of the dominant enemy species, so is a result of evenness itself. Human activity can degrade ecosystem function by reducing species number (richness) 1 , 2 , 3 , 4 and by skewing the relative abundance of species (evenness) 5 , 6 , 7 . Conservation efforts often focus on restoring or maintaining species number 8 , 9 , reflecting the well-known impacts of richness on many ecological processes 1 , 2 , 3 , 4 . In contrast, the ecological effects of disrupted evenness have received far less attention 7 , and developing strategies for restoring evenness remains a conceptual challenge 7 . In farmlands, agricultural pest-management practices often lead to altered food web structure and communities dominated by a few common species, which together contribute to pest outbreaks 6 , 7 , 10 , 11 . Here we show that organic farming methods mitigate this ecological damage by promoting evenness among natural enemies. In field enclosures, very even communities of predator and pathogen biological control agents, typical of organic farms, exerted the strongest pest control and yielded the largest plants. In contrast, pest densities were high and plant biomass was low when enemy evenness was disrupted, as is typical under conventional management. Our results were independent of the numerically dominant predator or pathogen species, and so resulted from evenness itself. Moreover, evenness effects among natural enemy groups were independent and complementary. Our results strengthen the argument that rejuvenation of ecosystem function requires restoration of species evenness, rather than just richness. Organic farming potentially offers a means of returning functional evenness to ecosystems.