Universität Bonn

INRES Crop Science

Amit Kumar Srivastava


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Dr. Amit Srivastava

Katzenburgweg 5

53115 Bonn

Dr. Amit Kumar Srivastava holds PhD in Agriculture Science with background in Crop and Cropping systems modelling, Agronomy, Forestry, and Agri-business-Management. He is  having experience of 11+ years as a postdoctoral researcher in Italy and in Germany with focus on Climate change impact on crop production; agricultural land use and food security; cropping system processes and responses to the management and climate; crop yield forecasting and Machine learning. He is currently working as a research scientist in the Institute of Crop Science and Resource Conservation, University of Bonn, and involved in agro-ecological modelling framework development (SIMPLACE) in connection with Upscaling Site-Specific Climate-Smart Agriculture and Land-use practices to improve regional production systems via integrated farm-system model development. He is also actively involved in the Agricultural Model Intercomparison and Improvement Project (AgMIP), an international, transdisciplinary project connecting climate science, crop modelling, and economic modelling to place regional agricultural impacts of climate change into their global economic context to assess uncertainties and vulnerability of world food security.

  • DAKIS22 : Digital Agricultural Knowledge and Information System, Innovative integration for landscape smart agriculture. 2019-2024 (BMBF funded)
  • Soil333 : Sustainable Subsoil Management. 2015-2024 (BMBF funded)
  • Sino-German Mobility Programme. 2020-2022 (DFG funded)
  • UPSCALERS44 : Upscaling Site-Specific Climate-smart Agriculture and Land use practices to Enhance Regional production Systems in West Africa. 2018-2022 (AU funded)
  • AgMIP55 : Agricultural Model Intercomparison and Improvement Project. 2017-present
  • Dong, J., Srivastava, A.K., et al., (2022). Simulation of dew point temperature in different time scales based on grasshopper algorithm optimized extreme gradient boosting. Journal of Hydrology. DOI:10.1016/jhydrol.2022.127452.
  • Srivastava, A.K., et al., (2022). Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data. Scientific Reports. DOI : 10.1038/s41598-022-06249-w.
  • Liu, S., Srivastava, A.K., et al., (2021). Simulating the Leaf Area Index of Rice from Multispectral Images. Remote Sens. 2021, 13(18), 3663; https://doi.org/10.3390/rs13183663
  • Liu, Y., Srivastava, A.K., et al., (2021). Strategy of subsurface pipe drainage system to alleviate soil salinization based on the DRAINMOD model. Irrigation and Drainage.  https://doi.org/10.1002/ird.2643
  • Hussain, J., Srivastava, A.K., et al., (2021): Effect of temperature on sowing dates of wheat under arid and semi-arid climatic regions and impact quantification through mechanistic modeling with evidence from field. MDPI Atmosphere. DOI: https://doi.org/10.3390/atmos12070927
  • Rahimi, J., Srivastava, A.K., et al., (2021): Modeling gas exchange and biomass production in West African Sahelian and Sudanian ecological zones. DOI: https://doi.org/10.5194/gmd-14-3789-2021
  • Zeng, D., Srivastava, A.K., et al., (2021): Time-delayed machine learning models for estimating groundwater depth in Hetao Irrigation District, China. Agricultural Water Management. DOI: https://doi.org/10.1016/j.agwat.2021.107032
  • Wallach, D., Srivastava, A.K., et al., (2021): The chaos in calibrating crop models: lessons learned from a multi-model calibration exercise. Environment Modelling and Software. DOI: 10.1016/j.envsoft.2021.105206
  • Laux, P., Srivastava, A.K., et al., (2021): To bias correct or not to bias correct? An agricultural impact modelers’ perspective on regional climate model data. Agricultural and Forest Meteorology. DOI: https://doi.org/10.1016/j.agrformet.2021.108406.
  • Lopez, G., Srivastava, A.K., et al., (2021): Effects of Recent Climate Change on Maize Yield in southwest Ecuador. Atmosphere. DOI: 10.3390/atmos12030299.
  • Dong, L., Srivastava, A.K., et al., (2021): Estimating the pan evaporation in Northwest China by coupling CatBoost with Bat algorithm. Water. DOI: 10.3390/w13030256.
  • Adusei, G., Srivastava, A.K., et al., (2021): The variability of grain yield of some cowpea genotypes in response to phosphorus and water stress under field conditions. Agronomy. DOI: 10.3390/agronomy11010028.
  • Liu, Y., Srivastava, A.K., et al., (2021): Simulating water and salt transport in subsurface pipe drainage systems with HYDRUS-2D. Journal of Hydrology. DOI.org/10.1016/j.jhydrol.2020.125823.
  • Zeng, W., Srivastava, A.K., et al., (2020): Parameter sensitivity and uncertainty analysis of radiation interception models for intercropping system. Ecological Chemistry and Engineering S. DOI: 10.2478/eces-2020-0028.
  • Falconier, G., Srivastava, A.K., et al., (2020): Modelling climate change impacts on maize yields under low N input conditions in sub-Saharan Africa. Global Change Biology. DOI: 10.1111/gcb.15261.
  • Wallach, D., Srivastava, A.K., et al., (2020): Multi model evaluation of phenology prediction for wheat in Australia. Agricultural and Forest Meteorology. DOI: 10.1016/j.agrformet.2020.108289.
  • Wallach, D., Srivastava, A.K., et al., (2020): How well do crop modeling groups predict wheat phenology, given calibration data from the target population? DOI:https://doi.org/10.1101/708578.
  • Zeng W., Srivastava, A.K., et al., (2020):  Effect of Vertically Heterogeneous Soil Salinity on Morphological Characteristics, Biomass Accumulation, Root Distribution, and Transpiration of Sunflower (Helianthus annuus L.). Journal of Animal & Plant Sciences, 30(6):1579-1595.
  • Srivastava, A.K et al., (2020): Implication of different sets of climate variables on regional Maize yield  simulations - A case study in Sub-Saharan Africa. Atmosphere,11,180; doi:10.3390/atmos11020180.
  • Tao, Fulu., Srivastava, A.K., et al., (2019): Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Agricultural and Forest Meteorology, 281,107851; doi:10.1016/j.agrformet.2019.107851.
  • Lopez, G., Srivastava, A.K., Gaiser, T., (2019): A Model-Based Estimation of Resource Use Efficiencies in Maize Production in Nigeria. Sustainability, 11, 5114; doi:10.3390/su11185114.
  • Kuhn, T., Srivastava, A.K., Gaiser, T., (2019): Coupling crop and bio-economic farm modelling to evaluate the revised fertilization regulations in Germany. Agricultural Systems, doi:10.1016/j.agsy.2019.102687.
  • Engida, E., Srivastava, A.K., Kuhn, A., Gaiser, T., (2019): Household welfare implications of better fertilizer access and lower use Inefficiency: Long-term scenarios for Ethiopia. Sustainability, 11, 3952; doi:10.3390/su11143952.
  • Srivastava, A.K., Mboh, C.M., Faye, B., Gaiser, T., Kuhn, A., Ermias, E., Ewert, F., (2019): Options for Sustainable Intensification of Maize Production in Ethiopia. Sustainability, 11, 1707; doi:10.3390/su11061707.
  • Kimball, B., Srivastava, A.K., et al., (2019): Simulation of Maize Evapotranspiration: An Inter-comparison among 29 Maize Models. Agricultural and Forest Meteorology, 271, 264-284.
  • Srivastava, A.K., Mboh, C.M., Gaiser, T., Ermias, E., Kuhn, A., Ewert, F., (2019): Effect of Mineral Fertilizer on Rain Water and Radiation use Efficiencies for Maize yield and biomass productivity in Ethiopia. Agricultural Systems,168, 88-100.
  • Maharjan, G.R., Srivastava, A.K., et al., (2019): Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates. European Journal of Agronomy,103, 32-46.
  • Mboh, C.M., Srivastava, A.K., Gaiser, T., Ewert, F., (2018): Impact of improved root length densities on grain yield and above ground biomass simulations of water-stressed spring wheat from  a 1D field scale model. Journal of Agronomy and Crop Science. DOI:10.1111/jac.12306
  • Hoffman, M., Srivastava, A.K., et al., (2017): How does inter-annual variability of attainable yield affect the magnitude of yield gaps for wheat and maize?An analysis at ten sites. Agricultural Systems. DOI:10.1016/j.agsy.2017.03.012
  • Srivastava, A.K., Mboh, C.M., Gaiser, T., Zhao, G., Ewert, F., (2017): Climate change impact under alternate realizations of climate scenarios on maize yield and biomass in Ghana. Agricultural Systems. DOI:10.1016/j.agsy.2017.03.011
  • Durand, J.L.., Srivastava, A.K., et al., (2017): How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield? European Journal of Agronomy, DOI: 10.1016/j.eja.2017.01.002
  • Srivastava, A.K., Mboh, C.M., Gaiser, T., Ewert, F., (2017): Impact of climatic variables on the spatial and temporal variability of crop yield and biomass gap in Sub-Saharan Africa – a case study in Central Ghana. Field Crops Research, 203, 33-46.
  • Srivastava, A.K., Mboh, C.M., Gaiser, T., Webber, H., Ewert, F., (2016): Effect of sowing date distributions on simulation of maize yields at regional scale – A case study in Central Ghana, West Africa. Agricultural Systems, 147, 10-23.
  • Srivastava, A.K., Gaiser, T., Ewert, F., (2016): Climate change impact and potential adaptation strategies   under alternate climate scenarios for Yam production in the sub-humid savannah zone of West Africa. Mitigation and Adaptation Strategies for Global Change, 21, 955-968.
  • Donatelli, M., Srivastava, A.K., Duvellier, G., Niemeyer, S., Fumagalli, D., (2015): Climate change   impact and potential adaptation strategies under alternate realizations of climate scenarios for major crops in Europe. Environmental Research Letters, 10, 1-12
  • Srivastava, A.K., Brackhage, C., Dudel, E. Gert., (2013): Prospects of Phytostabilization of Uranium Contaminations using Alnus glutinosa L. Journal of Plant Nutrition, DOI:10.1080/01904167.2013.766205.
  • Dagbenonbakin, G.D., Srivastava, A.K., Gaiser, T., Goldbach, H. (2013): Maize nutrient assessment in Benin Republic: Case of Upper Ouémé Catchment. Journal of Plant Nutrition, 36(4), 587-606.
  • Srivastava, A.K., Gaiser, T., Cornet, D., Ewert, F. (2012): Estimation of effective fallow availability for the prediction of yam productivity at the regional scale using model-based multiple scenario analysis. Field Crops Research. 131, 32-39.
  • Srivastava, A.K., Gaiser, T., Paeth, H., Ewert, F. (2012): The impact of climate change on yam (Dioscorea alata) yield in the savanna zone of West Africa. Agriculture, Ecosystems and Environment. 153, 57-64.
  • Dagbenonbakin, G.D., Srivastava, A.K., Gaiser, T., Goldbach, H. (2012): Diagnosis and Recommendation Integrated system: A tool for detecting nutrient deficiencies in Yam (Dioscorea rotundata). Journal of Plant Nutrition, 35(14), 2124-2134.
  • Srivastava, A.K., Gaiser, T. (2010): Simulating biomass accumulation and yield of yam (Dioscorea alata) in Upper Ouémé Basin (Benin Republic) - I. Compilation of physiological parameters and calibration at field scale. Field Crops Research. 116, 23-29.
  • Srivastava, A.K., Dagbenonbakin, G. D., Gaiser, T. (2010): Effect of fertilization on Yam (Dioscorea rotundata) biomass production.  Journal of Plant Nutrition 33 (7), 1056-1065.
  • MSc. Course Module: Crop and Ecosystem Analysis and Modelling

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