Abstract
We used the Decision Support System for Agro-technology Transfer-Cropping System Model (DSSAT) and data assimilation scheme (DSSAT-DA) to estimate maize (i.e., corn) yield and to evaluate the sensitivity of maize yield to hydroclimatic variables (i.e., precipitation, air temperatures, solar radiation, soil water). The remotely sensed soil moisture products, which includes Advanced Microwave Scanning Radiometer and the Soil Moisture and Ocean Salinity, were assimilated to DSSAT model by using the Ensemble Kalman Filtering approach. It was observed that both DSSAT and DSSAT-DA models can able to capture the annual trend of maize yield, although they overestimate the observed maize yield. The DSSAT-DA scheme assimilated with remotely sensed products slightly improves the model performance. The antecedent hydroclimatic information can influence the subsequent maize yield. The maize yield is sensitive to the soil water availability and precipitation amount, especially at the antecedent 1 month time to sowing and the subsequent second and third month’s growing period.
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References
Ahmed KF, Wang G, Yu M et al (2015) Potential impact of climate change on cereal crop yield in West Africa. Clim Chang 133:321–334. https://doi.org/10.1007/s10584-015-1462-7
Brocca L, Hasenauer S, Lacava T et al (2011) Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe. Remote Sens Environ 115:3390–3408. https://doi.org/10.1016/j.rse.2011.08.003
Curnel Y, de Wit AJW, Duveiller G, Defourny P (2011) Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment. Agric For Meteorol 151:1843–1855. https://doi.org/10.1016/j.agrformet.2011.08.002
de Wit AJW, van Diepen CA (2007) Crop model data assimilation with the ensemble Kalman filter for improving regional crop yield forecasts. Agric For Meteorol 146:38–56. https://doi.org/10.1016/j.agrformet.2007.05.004
Drusch M, Wood EF, Gao H (2005) Observation operators for the direct assimilation of TRMM microwave imager retrieves soil moisture. Geophys Res Lett 32:L15403. https://doi.org/10.1029/2005GL023623
Escorihuela MJ, Chanzy A, Wigneron JP et al (2010) Effective soil moisture sampling depth of L-band radiometry: a case study. Remote Sens Environ 114:995–1001. https://doi.org/10.1016/j.rse.2009.12.011
Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367. https://doi.org/10.1007/s10236-003-0036-9
Hansen JW, Challinor A, Ines A et al (2006) Translating climate forecasts into agricultural terms: advances and challenges. Clim Res 33:27–41. https://doi.org/10.3354/cr033027
Hoogenboom G, Jones JW, Wilkens PW et al (2015) Decision Support System For Agrotechnology Transfer (DSSAT). Version 4.6. www.DSSAT.net. DSSAT Foundation, Prosser, WA
Ines AVM, Das NN, Hansen JW et al (2013) Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sens Environ 138:149–164. https://doi.org/10.1016/j.rse.2013.07.018
Jones JW, Hoogenboom G, Porter C et al (2003) The DSSAT cropping system model. Eur J Agron 18:235–265. https://doi.org/10.1016/S1161-0301(02)00107-7
Kerr YH, Waldteufel P, Wigneron J-P et al (2010) The SMOS mission: new tool for monitoring key elements of the global water cycle. Proc IEEE 98:666–687. https://doi.org/10.1109/JPROC.2010.2043032
Koster RD, Dirmeyer PA, Guo ZC et al (2004) Regions of strong coupling between soil moisture and precipitation. Science 305:1138–1140. https://doi.org/10.1126/science.1100217
Koster RD, Guo ZC, Dirmeyer PA et al (2006) GLACE: the global land-atmosphere coupling experiment. Part I: overview. J Hydrometeor 7:590–610. https://doi.org/10.1175/JHM510.1
Li T, Hasegawa T, Yin X et al (2014) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob Chang Biol 21:1328–1341. https://doi.org/10.1111/gcb.12758
Liu D, Mishra AK (2017) Performance of AMSR_E soil moisture data assimilation in CLM4. 5 model for monitoring hydrologic fluxes at global scale. J Hydrol 547:67–79. https://doi.org/10.1016/j.jhydrol.2017.01.036
Liu D, Yu Z, Lv HS (2010) Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction. Water Sci Eng 3:361–377. https://doi.org/10.3882/j.issn.1674-2370.2010.04.001
Liu D, Wang GL, Mei R et al (2014a) Impact of initial soil moisture anomalies on climate mean and extremes over Asia. J Geophys Res 119:1–14. https://doi.org/10.1002/2013JD020890
Liu D, Wang GL, Mei R et al (2014b) Diagnosing the strength of land-atmosphere coupling at sub-seasonal to seasonal time scales in Asia. J Hydrometeorol 15:320–339. https://doi.org/10.1175/JHM-D-13-0104.1
Liu D, Mishra AK, Yu Z (2016) Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering. J Hydrol 538:243–255. https://doi.org/10.1016/j.jhydrol.2016.04.021
Liu D, Mishra AK, Yu Z et al (2017) Performance of SMAP, AMSR-E and LAI for weekly agricultural drought forecasting over continental United States. J Hydrol 553:88–104. https://doi.org/10.1016/j.jhydrol.2017.07.049
Lobell DB, Field CB (2007) Global scale climate-crop yield relationships and the impacts of recent warming. Environ Res Lett 2(1):014002. https://doi.org/10.1088/1748-9326/2/1/014002
Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333(6042):616–620. https://doi.org/10.1126/science.1204531
Lobell DB, Hammer GL, McLean G et al (2013) The critical role of extreme heat for maize production in the United States. Nat Clim Chang 3:497–501. https://doi.org/10.1038/nclimate1832
Long SP, Ainsworth EA, Leakey AD et al (2006) Food for thought: lower-than-expected crop yield stimulation with rising CO2 concentrations. Science 312(5782):1918–1921. https://doi.org/10.1126/science.1114722
Lu J, Carbone GJ, Gao P (2017) Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895–2014. Agric For Meteorol 237:196–208. https://doi.org/10.1016/j.agrformet.2017.02.001
Meza FJ, Silva D (2009) Dynamic adaptation of maize and wheat production to climate change. Clim Chang 94:143–156. https://doi.org/10.1007/s10584-009-9544-z
Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Mishra AK, Ines AVM, Singh VP et al (2013) Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation. Stoch Environ Res Risk Assess 27:449–457. https://doi.org/10.1007/s00477-012-0667-9
Mishra AK, Ines AVM, Das NN et al (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
Mishra AK, Vu T, Veettil AV et al (2017) Drought monitoring with soil moisture active passive (SMAP) measurements. J Hydrol 552:620–632. https://doi.org/10.1016/j.jhydrol.2017.07.033
Monteith JL, Moss CJ (1977) Climate and the efficiency of crop production in Britain. Philos Trans R Soc B 281:277–294. https://doi.org/10.1098/rstb.1977.0140
Njoku EG, Jackson TJ, Lakshmi V et al (2003) Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens 41:215–229. https://doi.org/10.1109/TGRS.2002.808243
Panu US, Sharma TC (2002) Challenges in drought research: some perspectives and future directions. Hydrol Sci J 47(S):S19–S30. https://doi.org/10.1080/02626660209493019
Porter JR, Semenov MA (2005) Crop responses to climatic variation. Philos Trans R Soc Lond B 360:2021–2035. https://doi.org/10.1098/rstb.2005.1752
Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–82
Reichle RH, Koster RD (2004) Bias reduction in short records of satellite soil moisture. Geophys Res Lett 31:L19501. https://doi.org/10.1029/2004GL020938
Reichle RH, Mclaughlin DB, Entekhabi D (2002) Hydrologic data assimilation with the ensemble Kalman filter. Mon Weather Rev 130:103–114
Ritchie SW, Hanway JJ (1982) How a corn plant develops. Iowa State Univ. (Special Report 48)
Rosenzweig C, Elliott J, Deryng D et al (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. PNAS 111(9):3268–3273. https://doi.org/10.1073/pnas.1222463110
Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. PNAS 106(37):15594–15598. https://doi.org/10.1073/pnas.0906865106
Singh VP (1997) The use of entropy in hydrology and water resources. Hydrol Process 11:587–626
Vazifedoust M, van Dam JC, Bastiaanssen WGM et al (2009) Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. Int J Remote Sens 30:2523–2545. https://doi.org/10.1080/01431160802552769
Villalobos FJ, Mateos L, Orgaz F et al (2002) Fitotecnia. Bases y tecnología de la producción agrícola, Mundi-Prensa, Madrid, Spain
Yan H, Moradkhani H, Zarekarizi M (2017) A probabilistic drought forecasting framework: a combined dynamical and statistical approach. J Hydrol 548:291–304. https://doi.org/10.1016/j.jhydrol.2017.03.004
Yin J, Zhan X, Zheng Y et al (2015) Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation. Geophys Res Lett 42:6710–6715. https://doi.org/10.1002/2015GL063366
Yu Z, Liu D, Lv HS et al (2012) A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter. J Hydrol 475:53–64. https://doi.org/10.1016/j.jhydrol.2012.08.034
Zhao C, Liu B, Piao S et al (2017) Temperature increase reduces global yields of major crops in four independent estimates. PNAS 114(35):9326–9331. https://doi.org/10.1073/pnas.1701762114
Zipper SC, Qiu JX, Kucharik CJ (2016) Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes. Environ Res Lett 11:094021. https://doi.org/10.1088/1748-9326/11/9/094021
Acknowledgements
This study was supported by the United States Department of Agriculture (USDA) award 2015-68007-23210, the National Natural Science Foundation of China (Grant No. 51709074), the Fundamental Research Funds for the Central Universities of China (Grant No. 2018B10414), the National Key Research and Development Program of China (Grant No. 2016YFC0402706), and the Special Fund of the State Key Laboratory of Hydrology-Water Resources (Grant No. 20145027312).
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Liu, D., Mishra, A.K. & Yu, Z. Evaluation of hydroclimatic variables for maize yield estimation using crop model and remotely sensed data assimilation. Stoch Environ Res Risk Assess 33, 1283–1295 (2019). https://doi.org/10.1007/s00477-019-01700-3
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DOI: https://doi.org/10.1007/s00477-019-01700-3