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Palatnik, R. R. (2014). Assessing The Impact Of Climate Change On Agriculture And A Water Economy With A Diverse Mix Of Water Types – The Israeli Case Study..
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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.
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Daccache, A. (2014). Assessing water and energy footprint of irrigated agriculture in the Mediterranean. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Agriculture in the Mediterranean, one of the water scarcest regions in the world is by far the largest water consuming sector. Dwindling water supply, increase in drought frequency and uncertainties associated with climate change have raised the alerts on the region’s food security and environmental sustainability. In this study, a large geo-database of global climate, soil and crop were combined with national irrigation statistics to run a water balance model to estimate the theoretical irrigation volumetric needs of the Mediterranean main strategic crops and their relative CO2 emissions. When associated with the reported crop yield and water resources availability, the spatial variability of water (m3/kg) and energy (CO2/kg) productivity across the Mediterranean region are obtained and vulnerable areas are identified. The estimated total water needs for the Mediterranean irrigated agriculture under current climate, land cover and irrigation methods was estimated to be around 46km3/year releasing more than 3Mt of CO2 in the atmosphere only from water abstraction and farm application. Currently, 59% of total irrigation water needs are located in catchments that are classified as under high and extremely high water risk. With climate change, water resources are expected to become scarcer and agriculture more dependent on irrigation to satisfy the continuous increase in food demand. Adaptation and mitigation options to tackle water scarcity and improve productivity under current and future climate will be discussed.
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Ben Touhami, H., & Bellocchi, G. (2014). Bayesian calibration of the Pasture Simulation model (PaSim) to simulate emissions from long-term grassland sites: a European perspective..
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Minet, J., Laloy, E., Tychon, B., & François, L. (2014). Bayesian inference of a dynamic vegetation model for grassland. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler. CARAIB is a mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions, and can thus be used for simulating grassland production under cutting or grazing management. Bayesian model inversion was performed at 4 grassland sites across Europe: Oensingen, CH; Grillenburg, DE; Laqueuille, FR and Monte-Bodone, IT. Four daily measured variables from these sites: the Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET) and Soil Water Content (SWC) were used to sample 10 parameters related to rooting depth, stomatal conductance, specific leaf area, carbon-nitrogen ratio and water stresses. The maximized likelihood function therefore involved four objectives, whereas the applied Bayesian framework allowed for assessing the so called parameter posterior probability density function (pdf), which quantifies model parameter uncertainty caused by measurement and model errors. Sampling trials were performed using merged data from all sites (all-sites-sampling) and for each site (site-specific sampling) separately. The derived posterior parameter pdfs from the all-sites sampling and site-specific sampling runs showed differences in relation with the specificities of each site. Analysis of these distributions also revealed model sensitivity to parameters conditioned on the measured data, as well as parameter correlations.
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