Estimating natural forest fire return interval in northeastern Ontario,Canada

Long-term forest fire regime was simulated for the Moose River Forest Management Unit (FMU) in northeastern Ontario. The simulated area has not been managed for timber production and fire suppression activity has been minimal. The available data included fire records for 1970–2006 and forest age structure from forest resource inventory completed in 1978. The fire regime was simulated using a simple percolation model driven by three parameters: probabilities of fire spread during low and high fire activity years and of a given year being a low fire activity year. The model successfully generated a long-term fire regime producing age structure and 37-year-long fire records similar to those observed for the Moose River FMU. The simulation results suggest that (a) fire return interval in northeastern Ontario is likely much shorter than indicated by estimates based exclusively on data from the last four decades of fire activity, and (b) it is possible that the fire regime in northeastern Ontario has not changed since mid-1800s but rather is characterized by relatively long periods of low incidence of fire interspersed with pulses of high fire activity.     

Forecasting landscape-scale,cumulative effects of forest management on vegetation and wildlife habitat: A case study of issues,limitations, and opportunities

Forest landscape disturbance and succession models have become practical tools for large-scale, long-term analyses of the cumulative effects of forest management on real landscapes. They can provide essential information in a spatial context to address management and policy issues related to forest planning, wildlife habitat quality, timber harvesting, fire effects, and land use change. Widespread application of landscape disturbance and succession models is hampered by the difficulty of mapping the initial landscape layers needed for model implementation and by the complexity of calibrating forest landscape models for new geographic regions. Applications are complicated by issues of scale related to the size of the landscape of interest (bigger is better), the resolution at which the landscape is modeled and analyzed (finer is better), and the cost or complexity of applying a landscape model (cheaper and easier is better). These issues spill over to associated analyses that build on model outputs or become integrated as auxiliary model capabilities. Continued development and application of forest landscape disturbance and simulation models can be facilitated by (1) cooperative efforts to initialize more and larger landscapes for model applications, (2) partnerships of practitioners and scientists to address current management issues, (3) developing permanent mechanisms for user support, (4) adding new capabilities to models, either directly or as compatible auxiliary models, (5) increasing efforts to evaluate model performance and compare multiple models running on the same landscape, and (6) developing methods to choose among complex, multi-resource alternatives with outputs that vary over space and time.     

Decentralized selection and participatory approaches in plant breeding for low-input systems

Heterogeneous environments make it difficult to apply consistent selection pressure because often it is difficult to identify a single or a few superior genotypes across all sets of conditions. However, when the target system is characterized by heterogeneity of environmental stress, varieties developed in high-yielding conditions may fail to satisfy farmers’ needs. Although this type of system is often found in marginal environments of developing countries, heterogeneous environmental conditions are also a feature of organic and low-external-input systems in developed countries. To meet the needs of these systems, breeding programs must decentralize selection, and although decentralized selection can be done in formal breeding programs, it is more efficient to involve farmers in the selection and testing of early generation materials. Breeding within these target systems is challenging, both genetically and logistically, but can identify varieties that are adapted to farming systems in marginal environments or that use very few external inputs. A great deal has been published in recent years on the need for local adaptation and participatory plant breeding; this article reviews and synthesizes that literature.     

A systematic review of the literature reveals trends and gaps in integrated pest management studies conducted in the United States

Integrated pest management (IPM) is a broad‐based approach for addressing pests that negatively affect human and environmental health and economic profitability. Weeds, insects and disease‐causing pathogens (diseases) are the pests most often associated with IPM. A systematic review, widely used in other scientific disciplines, was employed to determine the most commonly studied IPM topics and summarize the reasons for these trends and the gaps. In a field synopsis of the literature, 1679 relevant published papers were identified and categorized into one of the following five broad areas: IPM and organic (organic), climate change and pests (climate), rural and urban IPM (rural and urban), next‐generation education (education) and advanced production systems (technology). Papers were examined in greater detail for at least one of the three main pests in a systematic review. A majority (85%) of IPM papers have been in the area of rural and urban IPM, primarily addressing agriculture (78%). Professionals, landowners and the general public were the focus of a majority (95%) of IPM papers on education. Technology is an increasing area of focus in the literature. Over the past 40 years, IPM papers have primarily (75%) addressed insects and been limited mostly to rural and urban settings. Climate change, technology and education specific to pest management studies are increasingly being published and will help broaden the focus that could result in increased adoption and development of IPM. © 2017 Society of Chemical Industry     

Genetic variation in drought tolerance at seedling stage and grain yield in low rainfall environments in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Genetic architecture of seedling drought tolerance is complex and needs to be better understood. To address this challenge, we developed a protocol to identify the most promising drought-tolerant genotypes at the seedling stage in winter wheat. A population of 146 recombinant inbred lines (F₉) derived from a cross between wheat cultivars, ‘Harry’ (seedling drought tolerant) and ‘Wesley’ (seedling drought susceptible) were used in this study. All genotypes were sown in three replications in a randomized complete block design under controlled conditions in a greenhouse. Seven traits were scored and grouped into tolerance traits; days to wilting, leaf wilting, and stay green and survival traits; days to regrowth, regrowth, drought survival rate, and recovery after irrigation. Three selection indices were calculated (1) tolerance index, (2) survival index, and (3) drought tolerance index (DTI). The same set of genotypes were also tested for grain yield in two low rainfall environments for two seasons. High genetic variation was found among all genotypes for all seedling traits scored in this study. Correlations between tolerance and survival traits were weak or did not exist. Heritability estimates ranged from 0.53 to 0.88. DTI had significant phenotypic and genotypic correlations with all seedling traits. Genotypes were identified with a high drought tolerance at the seedling stage combined with high grain yield in low rainfall. Breeding for tolerance and survival traits should be taken into account for improving winter wheat drought tolerance at seedling stage. The selected genotypes can be used for to further improve drought tolerance in high yielding wheat for Nebraska.