Abstract
The response of two rainfed winter cereal yields (wheat and barley) to drought conditions in the Iberian Peninsula (IP) was investigated for a long period (1986–2012). Drought hazard was evaluated based on the multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) and three remote sensing indices, namely the Vegetation Condition (VCI), the Temperature Condition (TCI), and the Vegetation Health (VHI) Indices. A correlation analysis between the yield and the drought indicators was conducted, and multiple linear regression (MLR) and artificial neural network (ANN) models were established to estimate yield at the regional level. The correlation values suggested that yield reduces with moisture depletion (low values of VCI) during early-spring and with too high temperatures (low values of TCI) close to the harvest time. Generally, all drought indicators displayed greatest influence during the plant stages in which the crop is photosynthetically more active (spring and summer), rather than the earlier moments of plants life cycle (autumn/winter). Our results suggested that SPEI is more relevant in the southern sector of the IP, while remote sensing indices are rather good in estimating cereal yield in the northern sector of the IP. The strength of the statistical relationships found by MLR and ANN methods is quite similar, with some improvements found by the ANN. A great number of true positives (hits) of occurrence of yield-losses exhibiting hit rate (HR) values higher than 69% was obtained.
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References
Andrade C, Belo-Pereira M (2015) Assessment of droughts in the Iberian peninsula using the WASP-index. Atmos Sci Lett 16:208–218. https://doi.org/10.1002/asl2.542
Atzberger C, Formaggio AR, Shimabukuro YE, Udelhoven T, Mattiuzzi M, Sanchez GA, Arai E (2014) Obtaining crop-specific time profiles of NDVI : the use of unmixing approaches for serving the continuity between SPOT-VGT and PROBA-V time series. Int J Remote Sens 35:2615–2638. https://doi.org/10.1080/01431161.2014.883106
Austin RB, Cantero-Martínez C, Arrúe JL et al (1998) Yield-rainfall relationships in cereal cropping systems in the Ebro river valley of Spain. Eur J Agron 8:239–248. https://doi.org/10.1016/S1161-0301(97)00063-4
Beguería S, Vicente-Serrano SM, Reig F, Latorre B (2014) Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol 34:3001–3023. https://doi.org/10.1002/joc.3887
Belo-Pereira M, Dutra E, Viterbo P (2011) Evaluation of global precipitation data sets over the Iberian peninsula. J Geophys Res Atmos 116:1–16. https://doi.org/10.1029/2010JD015481
Blauhut V, Stahl K, Stagge JH, Tallaksen LM, de Stefano L, Vogt J (2016) Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors. Hydrol Earth Syst Sci 20:2779–2800. https://doi.org/10.5194/hess-20-2779-2016
Bokusheva R, Kogan F, Vitkovskaya I, Conradt S, Batyrbayeva M (2016) Satellite-based vegetation health indices as a criteria for insuring against drought-related yield losses. Agric For Meteorol 220:200–206. https://doi.org/10.1016/j.agrformet.2015.12.066
Capa-Morocho M, Rodríguez-Fonseca B, Ruiz-Ramos M (2016a) Sea surface temperature impacts on winter cropping systems in the Iberian peninsula. Agric For Meteorol 226–227:213–228. https://doi.org/10.1016/j.agrformet.2016.06.007
Capa-Morocho M, Ines AVM, Baethgen WE, Rodríguez-Fonseca B, Han E, Ruiz-Ramos M (2016b) Crop yield outlooks in the Iberian peninsula: connecting seasonal climate forecasts with crop simulation models. Agric Syst 149:75–87. https://doi.org/10.1016/j.agsy.2016.08.008
CLC (2012) Copernicus Programme Land Monitoring Service. https://land.copernicus.eu/pan-european/corine-lan
Cortesi N, Gonzalez-Hidalgo JC, Trigo RM, Ramos AM (2014) Weather types and spatial variability of precipitation in the Iberian peninsula. Int J Climatol 34:2661–2677. https://doi.org/10.1002/joc.3866
Dalezios NR, Blanta A, Spyropoulos NV, Tarquis AM (2014) Risk identification of agricultural drought for sustainable agroecosystems. Nat Hazards Earth Syst Sci 14:2435–2448. https://doi.org/10.5194/nhess-14-2435-2014
Di Falco S, Adinolfi F, Bozzola M, Capitanio F (2014) Crop insurance as a strategy for adapting to climate change. J Agric Econ 65:485–504. https://doi.org/10.1111/1477-9552.12053
Estes LD, Bradley BA, Beukes H, Hole DG, Lau M, Oppenheimer MG, Schulze R, Tadross MA, Turner WR (2013) Comparing mechanistic and empirical model projections of crop suitability and productivity: implications for ecological forecasting. Glob Ecol Biogeogr 22:1007–1018. https://doi.org/10.1111/geb.12034
FAO (2014) FAO Statistical Yearbook 2014: Europe and Central Asia food and agriculture. Food andAgriculture Organization of the United Nations, Budapest
FAO (2015) Statistical Pocketbook 2015: Food and Agriculture Organization of the United Nations. FAO, Rome
Ferrero R, Lima M, Gonzalez-Andujar JL (2014) Spatio-temporal dynamics of maize yield water constraints under climate change in Spain. PLoS One 9:e98220. https://doi.org/10.1371/journal.pone.0098220
Ferrise R, Moriondo M, Bindi M (2011) Probabilistic assessments of climate change impacts on durum wheat in the Mediterranean region. Nat Hazards Earth Syst Sci 11:1293–1302. https://doi.org/10.5194/nhess-11-1293-2011
García del Moral LF, Rharrabti Y, Villegas D, Royo C (2003) Evaluation of grain yield and its components in durum wheat under mediterranean conditions: An ontogenic approach. Agron J 95:266–274
García-Herrera R, Hernández E, Barriopedro D, Paredes D, Trigo RM, Trigo IF, Mendes MA (2007) The outstanding 2004/05 drought in the Iberian peninsula: associated atmospheric circulation. J Hydrometeorol 8:483–498. https://doi.org/10.1175/JHM578.1
Giménez L, Petillo MG, Paredes P, Pereira LS (2016) Predicting maize transpiration, water use and productivity for developing improved supplemental irrigation schedules in western Uruguay to cope with climate variability. Water 8:1–22. https://doi.org/10.3390/w8070309
Gouveia C, Trigo RM (2008) Influence of climate variability on European agriculture-analysis of winter wheat production. geoENV VI–Geostatistics Environ Appl Springer Netherlands 27:335–345. https://doi.org/10.3354/cr027135
Gouveia C, Trigo RM, Dacamara CC, Libonati R, Pereira JM (2008) The North Atlantic oscillation and European vegetation dynamics. Int J Climatol 28:1835–1847. https://doi.org/10.1002/joc.1682
Gouveia C, Trigo RM, DaCamara CC (2009) Drought and vegetation stress monitoring in Portugal using satellite data. Nat Hazards Earth Syst Sci 9:185–195
Gouveia C, Liberato MLR, DaCamara CC, Trigo RM, Ramos AM (2011) Modelling past and future wine production in the Portuguese Douro Valley. Clim Res 48:349–362. https://doi.org/10.3354/cr01006
Gouveia CM, Bastos A, Trigo RM, Dacamara CC (2012) Drought impacts on vegetation in the pre- and post-fire events over Iberian peninsula. Nat Hazards Earth Syst Sci 12:3123–3137. https://doi.org/10.5194/nhess-12-3123-2012
Gouveia CM, Páscoa P, Russo A, Trigo RM (2016) Land degradation trend assessment over Iberia during 1982–2012. Cuad Investig Geogr 42:89. https://doi.org/10.18172/cig.2945
Gouveia CM, Trigo RM, Beguería S, Vicente-Serrano SM (2017) Drought impacts on vegetation activity in the Mediterranean region: an assessment using remote sensing data and multi-scale drought indicators. Glob Planet Chang 151:15–27. https://doi.org/10.1016/j.gloplacha.2016.06.011
Grasso M, Feola G (2012) Mediterranean agriculture under climate change: adaptive capacity, adaptation, and ethics. Reg Environ Chang 12:607–618. https://doi.org/10.1007/s10113-011-0274-1
Hernández-Barrera S, Rodríguez-Puebla C (2017) Wheat yield in Spain and associated solar radiation patterns. Int J Climatol 37:45–58. https://doi.org/10.1002/joc.4975
Hernandez-Barrera S, Rodriguez-Puebla C, Challinor AJ (2017) Effects of diurnal temperature range and drought on wheat yield in Spain. Theor Appl Climatol 129:503–519. https://doi.org/10.1007/s00704-016-1779-9
Iglesias A, Quiroga S (2007) Measuring the risk of climate variability to cereal production at five sites in Spain. Clim Res 34:47–57. https://doi.org/10.3354/cr034047
Incerti G, Feoli E, Salvati L, Brunetti A, Giovacchini A (2007) Analysis of bioclimatic time series and their neural network-based classification to characterise drought risk patterns in South Italy. Int J Biometeorol 51:253–263. https://doi.org/10.1007/s00484-006-0071-6
Jiang D, Yang X, Clinton N, Wang N (2004) An artificial neural network model for estimating crop yields using remotely sensed information. Int J Remote Sens 25:1723–1732. https://doi.org/10.1080/0143116031000150068
Kogan FN (1990) Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int J Remote Sens 11:1405–1419. https://doi.org/10.1080/01431169008955102
Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Sp Res 15:91–100. https://doi.org/10.1016/0273-1177(95)00079-T
Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636. https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2
Kogan FN (2001) Operational space technology for global vegetation assessment. Bull Am Meteorol Soc 82:1949–1964. https://doi.org/10.1175/1520-0477(2001)082
Kogan F, Stark R, Gitelson A et al (2004) Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. Int J Remote Sens 25:2889–2896. https://doi.org/10.1080/01431160410001697619
Kogan F, Guo W, Strashnaia A, Kleshenko A, Chub O, Virchenko O (2015a) Modelling and prediction of crop losses from NOAA polar-orbiting operational satellites. Geomat Nat Haz Risk 7:886–900. https://doi.org/10.1080/19475705.2015.1009178
Kogan F, Goldberg M, Schott T, Guo W (2015b) Suomi NPP/VIIRS: improving drought watch, crop loss prediction, and food security. Int J Remote Sens 1161:1–11. https://doi.org/10.1080/01431161.2015.1095370
Le JA, El-Askary HM, Allali M, Struppa DC (2017) Application of recurrent neural networks for drought projections in California. Atmos Res 188:100–106. https://doi.org/10.1016/j.atmosres.2017.01.002
Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529:84–87. https://doi.org/10.1038/nature16467
Li Y, Ye W, Wang M, Yan X (2009) Climate change and drought: a risk assessment of crop-yield impacts. Clim Res 39:31–46
Martins DS, Raziei T, Paulo AA, Pereira LS (2012) Spatial and temporal variability of precipitation and drought in Portugal. Nat Hazards Earth Syst Sci 12:1493–1501. https://doi.org/10.5194/nhess-12-1493-2012
Martin-Vide J, Lopez-Bustins J (2006) The western Mediterranean oscillation and rainfall in the Iberian peninsula. Int J Climatol 26:1455–1475. https://doi.org/10.1002/joc.1388
Matsumura K, Gaitan CF, Sugimoto K et al (2015) Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. J Agric Sci 153:399–410. https://doi.org/10.1017/S0021859614000392
Mishra AK, Ines AVM, Das NN, Prakash Khedun C, Singh VP, Sivakumar B, Hansen JW (2015) Anatomy of a local-scale drought: application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. J Hydrol 526:15–29. https://doi.org/10.1016/j.jhydrol.2014.10.038
Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27:2103–2111. https://doi.org/10.1002/joc.1498
Muñoz-Díaz D, Rodrigo FS (2006) Seasonal rainfall variations in Spain (1912-2000) and their links to atmospheric circulation. Atmos Res 81:94–110. https://doi.org/10.1016/j.atmosres.2005.11.005
Nguyen T, Mula L, Cortignani R, Seddaiu G, Dono G, Virdis S, Pasqui M, Roggero P (2016) Perceptions of present and future climate change impacts on water availability for agricultural systems in the western Mediterranean region. Water 8:523. https://doi.org/10.3390/w8110523
Paredes P, Rodrigues GC, Alves I, Pereira LS (2014) Partitioning evapotranspiration, yield prediction and economic returns of maize under various irrigation management strategies. Agric Water Manag 135:27–39. https://doi.org/10.1016/j.agwat.2013.12.010
Paredes P, Rodrigues GC, Cameira M do R et al (2016) Assessing yield, water productivity and farm economic returns of malt barley as influenced by the sowing dates and supplemental irrigation. Agric Water Manag. https://doi.org/10.1016/j.agwat.2016.05.033
Páscoa P, Gouveia CM, Russo A, Trigo RM (2017) The role of drought on wheat yield interannual variability in the Iberian peninsula from 1929 to 2012. Int J Biometeorol 61:439–451. https://doi.org/10.1007/s00484-016-1224-x
Rodriguez-Puebla C, Encinas a H, Nieto S, Garmendia J (1998) Spatial and temporal patterns of annual precipitation variability over the Iberian peninsula. Int J Climatol 18:299–316. https://doi.org/10.1002/(SICI)1097-0088(19980315)18:3<299::AID-JOC247>3.0.CO;2-L
Rojas O, Vrieling A, Rembold F (2011) Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sens Environ 115:343–352. https://doi.org/10.1016/j.rse.2010.09.006
Ruiz-Ramos M, Mínguez MI (2010) Evaluating uncertainty in climate change impacts on crop productivity in the Iberian peninsula. Clim Res 44:69–82. https://doi.org/10.3354/cr00933
Russo A, Raischel F, Lind PG (2013) Air quality prediction using optimal neural networks with stochastic variables. Atmos Environ 79:822–830. https://doi.org/10.1016/j.atmosenv.2013.07.072
Russo A, Lind PG, Raischel F, Trigo R, Mendes M (2015) Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales. Atmos Pollut Res 6:540–549. https://doi.org/10.5094/APR.2015.060
Trigo RM, Añel JA, Barriopedro D et al (2013) The record winter drought of 2011 – 12 in the Iberian peninsula. Am Meteorol Soc:41–45
Van Hoolst R, Eerens H, Haesen D et al (2016) FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring. Int J Remote Sens 37:418–439. https://doi.org/10.1080/01431161.2015.1126378
Vicente-Serrano SM, Cuadrat-Prats JM, Romo A (2006) Early prediction of crop production using drought indices at different timescales and remote sensing data: application in the Ebro Valley (north-east Spain). Int J Remote Sens 27:511–518. https://doi.org/10.1080/01431160500296032
Vicente-Serrano SM, Begueria S, Lopez-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Vicente-Serrano SM, Lopez-Moreno J-I, Beguería S, Lorenzo-Lacruz J, Sanchez-Lorenzo A, García-Ruiz JM, Azorin-Molina C, Morán-Tejeda E, Revuelto J, Trigo R, Coelho F, Espejo F (2014) Evidence of increasing drought severity caused by temperature rise in southern Europe. Environ Res Lett 9:44001. https://doi.org/10.1088/1748-9326/9/4/044001
Wilhelmi OV, Wilhite DA (2002) Assessing vulnerability to agricultural drought : A nebraska case study. Nat Hazards 25:37–58
Wilks D (2006) Statistical methods in the atmospheric sciences, 2nd edn. Academic, Oxford
Wu H, Wilhite DA (2004) An operational agricultural drought risk assessment model for Nebraska, USA. Nat Hazards 33:1–21
Zampieri M, Ceglar A, Dentener F, Toreti A (2017) Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ Res Lett 12:64008. https://doi.org/10.1088/1748-9326/aa723b
Acknowledgements
Ana Russo and Andreia Ribeiro thank FCT for grants SFRH/BPD/99757/2014 and PD/BD/114481/2016, respectively. The authors are also sincerely thankful to Carlos C. DaCamara (Instituto Dom Luiz) for his valuable suggestions and support, and to two anonymous reviewers for their constructive comments.
Funding
This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMDROFLOOD (WaterJPI/0004/2014).
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Ribeiro, A.F.S., Russo, A., Gouveia, C.M. et al. Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices. Theor Appl Climatol 136, 203–220 (2019). https://doi.org/10.1007/s00704-018-2478-5
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DOI: https://doi.org/10.1007/s00704-018-2478-5