Presentation is loading. Please wait.

Presentation is loading. Please wait.

DROUGHT VULNERABILITY QUANTIFICATION IN BULGARIA THROUGH MODELING CROP PRODUCTIVITY AND IRRIGATION REQUIREMENTS Z. Popova*, М. Ivanova*, L.S.Pereira**,

Similar presentations


Presentation on theme: "DROUGHT VULNERABILITY QUANTIFICATION IN BULGARIA THROUGH MODELING CROP PRODUCTIVITY AND IRRIGATION REQUIREMENTS Z. Popova*, М. Ivanova*, L.S.Pereira**,"— Presentation transcript:

1 DROUGHT VULNERABILITY QUANTIFICATION IN BULGARIA THROUGH MODELING CROP PRODUCTIVITY AND IRRIGATION REQUIREMENTS Z. Popova*, М. Ivanova*, L.S.Pereira**, V.Alexandrov***, K. Doneva*, M. Kercheva*, P. Alexandrova* *N. Poushkarov Institute of Soil Science, Agrotechnology and Plant Protection-ISSAPPNP, 7 Shosse Bankya Str., 1080 Sofia, Bulgaria **CEER-Biosystems Engineering, Institute of Agronomy, Technical University of Lisbon, Tapada de Ajuda, Lisboa, Portugal *** National Institute of Meteorology and Hydrology- 66 Tsarigradsko chaussee Blvd., 1784 Sofia The objective is to assess the vulnerability of agriculture to drought in Bulgaria and South East Europe (SEE), using the WINISAREG model (Teixeira et al. 1992; Pereira et al. 2003) and high peak season SPI - index for the period Crop Materials and methods: Climate Simulation model Soil Crop coefficients Kc, depletion fraction p for no stress and yield response factor Ky (Allen et al., 1998) were validated using detailed independent datasets relative to long term experiments with maize under different irrigation schedules in Tsalapitsa, Plovdiv, Pustren and Zora, Stara Zagora, and Bojurishte, Sofia field ( Popova et al., 2006; Popova, 2008; Popova Pereira, 2010; Ivanova Popova, 2011). The WinISAREG model is an irrigation scheduling simulation tool for computing the soil water balance and evaluating the respective impacts on crop yields. The model adopts the water balance approach of Doorenbos & Pruitt (1977) and the updated methodology to compute crop evapotranspiration and irrigation requirements proposed by Allen et al. (1998). Table 2. Parameters of specific relationship y=a+bx between simulated relative yield decrease RYD (%) / net irrigation requirements NIRs (mm) and High Peak Season SPI2 “July-Aug” across soil groups and climate regions, Bulgaria, Table 1. Variability of rainfed maize grain yield characterized by the average value, kg ha-1, and the coefficient of variation Cv, %, in Bulgaria, Experimental fields of ISSNP and meteorological stations of NIMH in Bulgaria. Findings: Fig.1 .Evolution of High Peak Season (July-Aug) SPI2 at: a) Sofia and b) Plovdiv, (V.Koinov, I. Kabakchiev, K. Boneva, 1998) Soil map of Bulgaria Soil-Geographical regions in Bulgaria Monthly precipitation in June, July and August in an average demand year are the largest in Sofia field that is double than in Plovdiv, Varna, Lom, Sandanski. Results in Table 3, Soil map and a Map of TAW are used for mapping the SPI2“July-Aug” threshold. Monograph: RISK ASSESSMENT OF DROUGHT IN AGRICULTURE AND IRRIGATION MANAGEMENT THROUGH SIMULATION MODELS Fig.2 Average monthly precipitation for the average (probability of exceedance P=50%), wet (P=10%) and dry (P=90%) seasons at: a) Sofia, b) Plovdiv, May-Sept SPI2 “July-Aug” threshold under which soil moisture deficit leads to severe impacts on rainfed maize productivity in Bulgaria. Drought vulnerability Map: a) b) Fig.4 Comparison of Net irrigation requirements (NIRs) probability curves relative to six climate regions and soil group of medium TAW, mm m-1, Fig.5 Relationships between seasonal SPI2 “July-Aug” (X-axis) and relative yield decrease of rainfed maize RYD with Ky=1.6 (Y-axis) for: a) Plovdiv and b) Pleven; soils of large TAW (180 mm m-1), late maize hybrids. a) b) Fig.3 Net irrigation requirements (NIRs) probability of exceedance curves relative to soil groups of small, medium and large total available water (TAW) at: a) Sofia field; b) Plovdiv, South Bulgaria, a) a) b) a) b) c) e) f) d) Table 3. Economical threshold of High Peak Season SPI2 “July-Aug” (the average SPI2 for July and August) indicating the risk relative to rainfed maize in climate regions/soil groups in Bulgaria b) Fig.6 Probability of exceedance curve of relative yield decrease RYD for rainfed maize, Ky=1.6, soil of small TAW (116 mm m-1) at: a) Chelopechene, Sofia field, and b) Tsalapitsa, Plovdiv, a) b) h) i) g) Spatial distribution of SPI2 “July-Aug” a)-c)/ relative yield decrease for rainfed maize (RYD, %) d)-f)/ net irrigation requirements (NIR, mm) g)-i) for the year of: a), d) g) extreme (2000); b), e) h) average (1970) and c), f) i) moderate (1981) irrigation demand, Bulgaria Acknowledgements We gratefully acknowledge the financial support of Drought Management Center for South East Europe Project, South East Europe Transnational Cooperation Programme co-funded by the European Union, for implementation and dissemination of our studies’ results on crop vulnerability to droughts and irrigation management in Bulgaria. Fig. 7 Probability exceedance curves of RYD of rainfed maize for soil of small, medium and large TAW, Ky=1.6, at: a) Plovdiv, South and b) Pleven, North Bulgaria, late hybrids (H708, 2L602, BC622), Fig.8 Comparison of relative yield decrease (RYD, %) probability of exceedance curves, Ky = 1.6, six climate regions and two soil groups of: a) medium ( ) and b) large (180 mm m-1) TAW, rainfed maize, EGU General Assembly April 2012 Vienna, Austria


Download ppt "DROUGHT VULNERABILITY QUANTIFICATION IN BULGARIA THROUGH MODELING CROP PRODUCTIVITY AND IRRIGATION REQUIREMENTS Z. Popova*, М. Ivanova*, L.S.Pereira**,"

Similar presentations


Ads by Google