|
|
Legarrea, S., Velázquez, E., Aguado, P., Fereres, A., Morales, I., Rodríguez, D., et al. (2014). Effects of a photoselective greenhouse cover on the performance and host finding ability of Aphidius ervi in a lettuce crop. BioControl, 59(3), 265–278.
Abstract: In the search for a durable pest control management, biological control agents and photoselective covers are suitable candidates to be implemented in greenhouse crops. In this work, we studied the effects of a 50 mesh photoselective cover compared to a standard with similar characteristics but without UV-absorbing additives on the performance of Aphidius ervi Haliday (Hymenoptera: Braconidae), a widely used parasitoid to control aphids in vegetable crops. Four field experiments were conducted in La Poveda Experimental Farm (Central Spain) where a lettuce crop was grown during the years 2008-2010. Lettuce plants were infested by Macrosiphum euphorbiae (Thomas) (Hemiptera: Aphididae) and the parasitoid A. ervi was released and monitored throughout the crop cycle to evaluate any constraint in its performance produced by UV-absorbing nets. The ability of A. ervi to find and parasitize the host was not modified by the photoselective cover during the four seasons studied. Thus, we suggest that both strategies could be combined in the context of IPM in vegetable crops where this natural enemy is released.
|
|
|
|
Dumont, B., Leemans, V., Ferrandis, S., Bodson, B., Destain, J. - P., & Destain, M. - F. (2014). Assessing the potential of an algorithm based on mean climatic data to predict wheat yield. Precision Agric., 15(3), 255–272.
Abstract: The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.
|
|
|
|
Ma, S., Lardy, R., Graux, A. - I., Ben Touhami, H., Klumpp, K., Martin, R., et al. (2015). Regional-scale analysis of carbon and water cycles on managed grassland systems. Env. Model. Softw., 72, 356–371.
Abstract: Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213 g C m(-2) yr(-1), which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185 g C m(-2) yr(-1) on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006 g C m(-2) yr(-1), respectively, in agreement with observed average GPP (1230 g C m(-2) yr(-1)) and RECO (1046 g C m(-2) yr(-1)). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was similar to 5-16 g C week(-1) across the study sites, while the goodness of fit (R-2) was similar to 0.4-0.9. For evapotranspiration (ET), the average value of simulated ET (415 mmyr(-1)) for all sites and years is close to the average value of the observed ET (451 mm yr(-1)) by flux towers (on a weekly basis, RMSE similar to 2-8 mm week(-1); R-2 = 0.3-0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system.
|
|
|
|
Tao, F., Roetter, R. P., Palosuo, T., Hernandez Diaz-Ambrona, C. G., Ines Minguez, M., Semenov, M. A., et al. (2018). Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Chang. Biol., 24(3), 1291–1307.
Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
|
|
|
|
Molina-Herrera, S., Haas, E., Grote, R., Kiese, R., Klatt, S., Kraus, D., et al. (2017). Importance of soil NO emissions for the total atmospheric NOX budget of Saxony, Germany. Atm. Environ., 152, 61–76.
Abstract: Soils are a significant source for the secondary greenhouse gas NO and assumed to be a significant source of tropospheric NOx in rural areas. Here we tested the LandscapeDNDC model for its capability to simulate magnitudes and dynamics of soil NO emissions for 22 sites differing in land use (arable, grassland and forest) and edaphic as well as climatic conditions. Overall, LandscapeDNDC simulated mean soil NO emissions agreed well with observations (r(2) = 0.82). However, simulated day to day variations of NO did only agree weakly with high temporal resolution measurements, though agreement between simulations and measurements significantly increased if data were aggregated to weekly, monthly and seasonal time scales. The model reproduced NO emissions from high and low emitting sites, and responded to fertilization (mineral and organic) events with pulse emissions. After evaluation, we linked the LandscapeDNDC model to a GIS database holding spatially explicit data on climate, land use, soil and management to quantify the contribution of soil biogenic NO emissions to the total NOx budget for the State of Saxony, Germany. Our calculations show that soils of both agricultural and forest systems are significant sources and contribute to about 8% (uncertainty range: 6 -13%) to the total annual tropospheric NO, budget for Saxony. However, the contributions of soil NO emission to total tropospheric NO, showed a high spatial variability and in some rural regions such as the Ore Mts., simulated soil NO emissions were by far more important than anthropogenic sources. (C) 2016 Elsevier Ltd. All rights reserved.
|
|
