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Agroclim-Map, a GIS Application for Agroclimatic Systems Analysis GONZÁLEZ C. A.(1), MOUTAHIR H.(2), HERRERA M.(1), ZAYAS L.(1), TOUHAMI I.(2) & BELLOT J.F.(2) (1)

The Higher Institute of Technologies and Applied Sciences (InSTEC), Havana, Cuba, [email protected] (2) Department of Ecology, University of Alicante, Alicante, Spain, [email protected]

Abstract. This work gives a technical description of Agroclim-Map which is a GIS application for agroclimatic systems analysis. Agroclim-Map is software which allows the computation of various agroclimatic models and indexes. The outputs of these models are used as inputs for post processing methods (statistic and geostatistic processing). The final products are tables, graphics and maps which make the interpretation of the analysis results easier. As an application of the AgroclimMap software we present a case study of the spatial distribution and temporal variability of the potential productivity index (PPI) of some crops at a regional scale in the province of Alicante (SE Spain) under different climate change scenarios. Keywords: Agroclim-Map, GIS application, Agroclimatic system 1. Introduction. Growth and development of field crops are strongly affected by the climate conditions as well as by extreme weather events. These may have severe consequences both for crops directly and for key field operations (i.e. sowing and harvest timing). Farming will remain weather-dependent activity even under changed climate conditions [1]. The internet provides free access to masses of information about climate and the environment, and increasingly accurate weather and climate predictions. But ways are needed to better translate raw information into accessible forms that are understandable and relevant to agricultural decision-making [2]. The tool Agroclim-Map allows the calculation of different agro meteorological indexes from time series data measured or predicted at stations or specific points, the resulting information is handled using post-processing optimization methods (statistic and geostatistic) to make finals products (like tables or maps) with the integrated information, thus the interpretation and analysis result easier. In this work we present the software Agroclim-Map and its application to a case study: the spatial distribution and temporal variability of the potential productivity index (PPI) for barley (Hordeum vulgare) and winter wheat (Triticum aestivum L.) at a regional scale in the province of Alicante (SE Spain) under different climate change scenarios. 2 Technical description of Agroclim-Map Agroclim-Map is carried out using a modular philosophy through the paradigm of object-oriented programming so that new modules can be easily added. Also special care was taken in the design of graphic user interface to be easy and intuitive without dismiss classic format of geographic information systems. 2.1 Data structure In general, indexes and models present in Agroclim-Map did not require many parameters, making possible their use in areas with lack of information, developing countries and under climate change scenarios. The data is structured as follows: I-Domain Data: frontier, polygons and points or stations; II-Stations Data: time series of meteorological data associated with the stations (eg precipitation, reference evapotranspiration, temperature) and soils data (eg, field capacity, soil type); III-Crop Data: physiology of crops to be analyzed (eg, root depth, crop coefficient and the factor of effect of phenological stage on yield, agricultural calendar); IV-Raster Map: used as output and at internal procedures. Modules were implemented for graphical editing of data, as a basic editor for custom vector maps, an editor of agricultural calendars and tables for the entry of the climatic series, and soil- plant parameters. 2.2 Agro-climatic indexes The following indexes are available in the Agroclim-Map: I-Potential Productivity Index [3]; II-Drought Index [4], IIIAgricultural Risk [5], and IV-Sum of temperature. Others may be introduced by adding modules of source code or using the calculator provided by the software. 2.3 Post-processing methods The application allows characterizing the system soil-plant-atmosphere under different conditions; postprocessing methods proposed are based on trying to determine any conditions that maximize or minimize an objective function (eg, yields, agricultural drought), they will be more representative as larger and better quality is the database. The calculator of Agroclim-Map also allows optimization procedures customized by the user using the functions max and min. Some of the more representative methods have the crop yield potential as objective function, to determine: I- optimal zone: area of the map that reflects higher potential (usually higher than a provided threshold); II- optimal crop and plantation date: raster maps where each cell represent respectively the date of planting and crop, that maximizes yields, IIIoptimal crop rotation: raster map where each cell represent the crop rotation (including fallow periods) to get higher yield potential.

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In addition, different statistical indicators can be used to synthesize in one map, existing information on several maps. Some of these statisticians are: measures of central tendency, cumulative probability and the coefficient of variation. The procedures to generate these results should be made with sufficient number of years if it is to be representative of the soil-plant-atmosphere under study. 3 Case study In order to illustrate some applications of Agroclim-Map we analyzed the spatial distribution and temporal variability of the potential productivity index (PPI) for barley (Hordeum vulgare) and winter wheat (Triticum aestivum L.) at a regional scale in the province of Alicante (SE Spain) under different climate change scenarios.The regional climate scenarios used in this study are made by the National Institute of Meteorology of Spain (INM) from the outputs of the HadCM3 model for SRES emission scenarios A2, with the statistical scale reduction method SDSM (Statistic Downscaling Method), in the periods 1961-1990, 2011-2040, 2041-2070 and 2071-2099. 3.1 PPI trend analysis PPI was calculated for wheat and barley (fig.1) in the province of Alicante. Wheat yields were estimated by regression curve [6] and the yield percent of variation in each future period was calculated (fig 2). In both cases there is a trend to lower productivity in the future, and this reduction affects the entire province resulting more pronounced in the northeastern part.

Fig. 1 Potential Productivity Index of barley in base and estimate periods

Fig. 2 Variation in wheat yields (percent) in each period 3.2 Probabilistic analysis of the annual variation in wheat yields It compared yields of each year with the average in the base period, to compute the annual change in percent. The nonexceedance probability according to the Weibull distribution allows knowing the probability of variation lower than a selected threshold (fig. 3): • The likelihood of yields decrease (variation less than zero) is 55% in the period 2011-2040 and 74% in 2041-2070, while there's 93.3% of probability of yields decline over 8% in the period 2071-2099. • Under the selected emissions scenario it is very likely (63.3%) that wheat productivity in the province of Alicante in the last quarter of the century decreases at least by 16% as a result of water deficit. • Decreases in yields of up to 22% can be expected as a consequence of the great pressure of water stress

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Yields variation (%) Fig. 3 Probability of non-exceedance of the annual variation in wheat yields 4 Conclusions. The number of analysis and calculations that the Agroclim-Map allows, the internal structure and the graphical user interface of this software make it as a good GIS application for the agroclimatic systems analysis. The example of the PPI analysis at the province of Alicante scale, presented in this work, showed some of the potentialities of the Agroclim-Map software. Acknowledgements The authors would like to express thanks to the Spanish Agency for International Development Cooperation (AECID) for a scholarship and to the Plan Nacional, I+D+I (CGL2011-30531-C03-01) for the financial support that allowed carrying out this study. References [27] M. Trnka, P. Stepánek, M. Dubrovský, D. Semerádová, J. Eitzinger, H. Formayer, Z. Zalud y M.Mozný, “Expected change of the Key Agrometeorological Parameters in the Central Europe between 2025 and 2050”, 7th EMS Annual Meeting & 8th European Conference on Applications of Meteorology, 2007, [poster (PDF)] [28] J. Hansen y K. Coffey, “Agro-climate tools for a new climate-smart agriculture”, [International Research Institute for Climate and Society Policy Brief, International Research Institute for Climate and Society (IRI)], Columbia University, New York, USA (2011), 4 pp. [29] J. Doorenbos, J.M.G.A. Plusje, A.H Kassam, V. Branscheid y C.L.M Bentvelsen,, “Yield response to water. Irrigation and Drainage Paper No. 33”, Food and Agriculture Organization of the United Nations (FAO), Roma (1986), 192 pp. [30] O. Solano, R. Vázquez y J.A. Menéndez, “The wet index modified and its application in the surveillance and emission of early warnings to Cuban farmers”, XI National Congress of the Mexican Meteorological Organization, 2001, (Ref: pon. 59 htm. 9 pp.). [31] R. Gommes, “Climate-related risk in agriculture”, A note prepared for the IPCC Expert Meeting on Risk Management Methods, Toronto (1998), 13 pp. [Ref] [32] C. A. González, “Contribution to the analysis of agricultural systems. Case study”, Degree Thesis, InSTEC, 2010, (pp. 3840).

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