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Lipiec, J., Doussan, C., Nosalewicz, A., & Kondracka, K. (2013). Effect of drought and heat stresses on plant growth and yield: a review. International Agrophysics, 27(4), 463–477.
Abstract: Drought and heat stresses are important threat limitations to plant growth and sustainable agriculture worldwide. Our objective is to provide a review of plant responses and adaptations to drought and elevated temperature including roots, shoots, and final yield and management approaches for alleviating adverse effects of the stresses based mostly on recent literature. The sections of the paper deal with plant responses including root growth, transpiration, photosynthesis, water use efficiency, phenotypic flexibility, accumulation of compounds of low molecular mass (eg proline and gibberellins), and expression of some genes and proteins for increasing the tolerance to the abiotic stresses. Soil and crop management practices to alleviate negative effects of drought and heat stresses are also discussed. Investigations involving determination of plant assimilate partitioning, phenotypic plasticity, and identification of most stress- tolerant plant genotypes are essential for understanding the complexity of the responses and for future plant breeding. The adverse effects of drought and heat stress can be mitigated by soil management practices, crop establishment, and foliar application of growth regulators by maintaining an appropriate level of water in the leaves due to osmotic adjustment and stomatal performance.
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Perego, A., Giussani, A., Fumagalli, M., Sanna, M., Chiodini, M., Carozzi, M., et al. (2013). Crop rotation, fertilizer types and application timing affecting nitrogen leaching in nitrate vulnerable zones in Po Valley. Italian Journal of Agrometeorology, 3(2), 39–50.
Abstract: A critical analysis was performed to evaluate the potential risk of nitrate leaching towards groundwater in three Nitrate Vulnerable Zones (NVZs) of the Lombardia plain by applying the ARMOSA crop simulation model over a 20 years period (1988-2007). Each studied area was characterized by (i) two representative soil types, (ii) a meteorological data set, (iii) four crop rotations according to the regional land use, (iv) organic N load, calculated on the basis of livestock density. We simulated 3 scenarios defined by different fertilization time and amount of mineral and organic fertilizers. The A scenario involved no limitation in organic N application, while under the B and C scenarios the N organic amount was 170 and 250 kg N ha(-1)y(-1), respectively. The C scenario was compliant with the requirement of the 2012 Italian derogation, allowing only the use of organic manure with an efficiency greater than 65%. The model results highlighted that nitrate leaching was significantly reduced passing from the A scenario to the B and C ones (p<0.01); on average nitrogen losses decreased by up to 53% from A to B and up to 75% from A to C.
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Perego, A., Giussani, A., Sanna, M., Fumagalli, M., Carozzi, M., Alfieri, L., et al. (2013). The ARMOSA simulation crop model: overall features, calibration and validation results. Italian Journal of Agrometeorology, 3, 23–38.
Abstract: ARMOSA is a dynamic simulation model which was developed to simulate crop growth and development, water and nitrogen dynamics under different pedoclimatic conditions and cropping systems in the arable land. The model is meant to be a tool for the evaluation of the impact of different crop management practices on soil nitrogen and carbon cycles and groundwater nitrate pollution. A large data set collected over three to six years from six monitoring sites in Lombardia plain was used to calibrate and validate the model parameters. Measured meteorological data, soil chemical and physical characterizations, crop-related data of different cropping systems allowed for a proper parameterization. Fit indexes showed the reliability of the model in adequately predicting crop-related variables, such as above ground biomass (RRMSE=11.18, EF=0.94, r=0.97), Leaf Area Index maximum value (RRMSE=8.24, EF=0.37, r=0.72), harvest index (RRMSE=19.4, EF=0.32, r=0.74), and crop N uptake (RRMSE=20.25, EF=0.69, r=0.85). Using two different one-year data set from each monitoring site, the model was calibrated and validated, getting to encouraging results: RRMSE=6.28, EF=0.52, r=0.68 for soil water content at different depths, and RRMSE=34.89, EF=0.59, r=0.75 for soil NO3-N content along soil profile. The simulated N leaching was in full agreement with measured data (RRMSE=26.62, EF=0.88, r=0.98).
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Refsgaard, J. C., Arnbjerg-Nielsen, K., Drews, M., Halsnaes, K., Jeppesen, E., Madsen, H., et al. (2013). The role of uncertainty in climate change adaptation strategies – a Danish water management example. Mitig. Adapt. Strateg. Glob. Change, 18(3), 337–359.
Abstract: We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could be used to address concerns that the IPCC approach is oversimplified. We have studied the role of uncertainty in climate change adaptation planning using examples from four Danish water related sectors. The dominating sources of uncertainty differ greatly among issues; most uncertainties on impacts are epistemic (reducible) by nature but uncertainties on adaptation measures are complex, with ambiguity often being added to impact uncertainties. Strategies to deal with uncertainty in climate change adaptation should reflect the nature of the uncertainty sources and how they interact with risk level and decision making: (i) epistemic uncertainties can be reduced by gaining more knowledge; (ii) uncertainties related to ambiguity can be reduced by dialogue and knowledge sharing between the different stakeholders; and (iii) aleatory uncertainty is, by its nature, non-reducible. The uncertainty cascade includes many sources and their propagation through technical and socio-economic models may add substantially to prediction uncertainties, but they may also cancel each other. Thus, even large uncertainties may have small consequences for decision making, because multiple sources of information provide sufficient knowledge to justify action in climate change adaptation.
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Leclère, D., Jayet, P. - A., & de Noblet-Ducoudré, N. (2013). Farm-level Autonomous Adaptation of European Agricultural Supply to Climate Change. Ecol. Econ., 87, 1–14.
Abstract: The impact of climate change on European agriculture is subject to a significant uncertainty, which reflects the intertwined nature of agriculture. This issue involves a large number of processes, ranging from field to global scales, which have not been fully integrated yet. In this study, we intend to help bridging this gap by quantifying the effect of farm-scale autonomous adaptations in response to changes in climate. To do so, we use a modelling framework coupling the STICS generic crop model to the AROPAj microeconomic model of European agricultural supply. This study provides a first estimate of the role of such adaptations, consistent at the European scale while detailed across European regions. Farm-scale autonomous adaptations significantly alter the impact of climate change over Europe, by widely alleviating negative impacts on crop yields and gross margins. They significantly increase European production levels. However, they also have an important and heterogeneous impact on irrigation water withdrawals, which exacerbate the differences in ambient atmospheric carbon dioxide concentrations among climate change scenarios. (c) 2012 Elsevier B.V. All rights reserved.
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