Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2021
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Pollination sustains biodiversity and food security, but pollinators are threatened by habitat degradation, fragmentation, and loss. We assessed how remaining forest influenced bee visits to flowers in an oil palm-dominated landscape in Borneo. We observed bee visits to six plant species: four crops (Capsicum frutescens L. “chili”; Citrullus lanatus (Thunb.) Matsum & Nakai “watermelon”; Solanum lycopersicum L. “tomato”; and Solanum melongena L. “eggplant”); one native plant Melastoma malabathricum L. “melastome”; and the exotic Turnera subulata Smith “turnera”. We made one local grid-based and one landscape-scale transect-based study spanning 208 and 2130 m from forest, respectively. We recorded 1249 bee visits to 4831 flowers in 1046 ten-min observation periods. Visit frequency varied among plant species, ranging from 0 observed visits to S. lycopersicum to a mean of 0.62 visits per flower per 10 min to C. lanatus. Bee visitation frequency declined with distance from forest in both studies, with expected visitation frequency decreasing by 55% and 66% at the maximum distance from forest in each study. We also tested whether the distance to the nearest oil palm patch, with a maximum distance of 144 m, influenced visitation, but found no such associations. Expected visitation frequency was 70%–77% lower for plants close to a 200 ha forest fragment compared with those near large continuous forests (>400 ha). Our results suggest that, although found throughout the oil palm-dominated landscape, bees depend on remaining forests. Larger forests support more bees, though even a 50 ha fragment has a positive contribution. Abstract in Indonesian is available with online material.
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Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.
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Authors
Cornelya KlutschAbstract
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Trygve S. AamlidAbstract
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One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers’ N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers’ N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice.
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Lampros LamprinakisAbstract
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