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Coucheney, E., Buis, S., Launay, M., Constantin, J., Mary, B., García de Cortázar-Atauri, I., et al. (2015). Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Env. Model. Softw., 64, 177–190.
Abstract: Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved.
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Dáder, B., Plaza, M., Fereres, A., & Moreno, A. (2015). Flight behaviour of vegetable pests and their natural enemies under different ultraviolet-blocking enclosures. Ann. Appl. Biol., 167(1), 116–126.
Abstract: Ultraviolet (UV) radiation, particularly in the UV-A + B range (280-400 nm) is a fraction of the solar spectrum that regulates almost every aspect of insect behaviour, including orientation towards hosts, alighting, arrestment and feeding behaviour. To study the role of UV radiation on the flight activity of five insect species of agricultural importance (pests Myzus persicae, Bemisia tabaci and Tuta absoluta, and natural enemies Aphidius colemani and Sphaerophoria rueppellii), one-chamber tunnels were covered with six cladding materials with different light transmittance properties ranging from 2% to 83% UV and 54% to 85% photosynthetically active radiation (PAR). Inside each tunnel, insects were released from tubes placed in a platform suspended from the ceiling. Specific targets varying with insect species were placed at different distances from the platform. Evaluation parameters were designed for each insect and tested separately. The ability of insects to leave the platform was assessed, as well as the number of captures, eggs or mummies in each target, either sticky traps or plants. Our results suggest differences in flight activity among insect species and UV-blocking nets. The UV-opaque film drastically prevented aphids, and whiteflies from flying outside the tubes whereas T. absoluta, syrphids and parasitoids were not affected. Aphid flight behaviour was affected by the UV-opaque film compared to the other nets, especially in the furthest target of the tunnel. Fewer aphids reached distant traps under UV-absorbing nets, and significantly more aphids could fly to the end of tunnels covered with non-UV-blocking materials. Orientation of B. tabaci and T. absoluta was also negatively affected by the UV-opaque film although in a different trend. Unlike aphids, differences in B. tabaci captures were mainly found in the closest targets. UV transmittance did not have any effects on parasitoids, and S. rueppellii, implying cues other than visual for these insects under our experimental conditions. Further effects of photoselective enclosures on greenhouse pests and their natural enemies are discussed.
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Caubel, J., García de Cortázar-Atauri, I., Launay, M., de Noblet-Ducoudré, N., Huard, F., Bertuzzi, P., et al. (2015). Broadening the scope for ecoclimatic indicators to assess crop climate suitability according to ecophysiological, technical and quality criteria. Agricultural and Forest Meteorology, 207, 94–106.
Abstract: The cultivation of crops in a given area is highly dependent of climatic conditions. Assessment of how the climate is favorable is highly useful for planners, land managers, farmers and plant breeders who can propose and apply adaptation strategies to improve agricultural potentialities. The aim of this study was to develop an assessment method for crop-climate suitability that was generic enough to be applied to a wide range of issues and crops. The method proposed is based on agroclimatic indicators that are calculated over phenological periods (ecoclimatic indicators). These indicators are highly relevant since they provide accurate information about the effect of climate on particular plant processes and cultural practices that take place during specific phenological periods. Three case studies were performed in order to illustrate the potentialities of the method. They concern annual (maize and wheat) and perennial (grape) crops and focus on the study of climate suitability in terms of the following criteria: ecophysiological, days available to carry out cultural practices, and harvest quality. The analysis of the results revealed both the advantages and limitations of the method. The method is general and flexible enough to be applied to a wide range of issues even if an expert assessment is initially needed to build the analysis framework. The limited number of input data makes it possible to use it to explore future possibilities for agriculture in many areas. The access to intermediate information through elementary ecoclimatic indicators allows users to propose targeted adaptations when climate suitability is not satisfactory.
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Baranowski, P., Jedryczka, M., Mazurek, W., Babula-Skowronska, D., Siedliska, A., & Kaczmarek, J. (2015). Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS One, 10(3), e0122913.
Abstract: In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%).
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Watson, J., Challinor, A. J., Fricker, T. E., & Ferro, C. A. T. (2015). Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model. Clim. Change, 132(1), 93–109.
Abstract: Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.
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