PLANT BIOLOGY
ANIMAL BIOLOGY
SUBSCRIPTION
E-SUBSCRIPTION
 
MAP
MAIN PAGE

 

 

 

 

Yakushev V.P., Yakushev V.V., Badenko V.L., Matveenko D.A., Chesnokov Yu.V. Productivity based on mass calculations of the agroecosystem simulation model in geoinformation environment (review). Sel'skokhozyaistvennaya Biologiya [Agricultural Biology], 2020, Vol. 55, № 3, p. 451-467.
(how to cite this article)

doi: 10.15389/agrobiology.2020.3.451eng

UDC: 631.5:519.3

Acknowledgements:
Supported financially from the federal budget under the Agreement on subsidies of December 10, 2019 No. 05.607.21.0302, the project unique identifier RFMEFI60719X0302

 

OPERATIVE AND LONG-TERM FORECASTING OF CROP PRODUCTIVITY BASED ON MASS CALCULATIONS OF THE AGROECOSYSTEM SIMULATION MODEL IN GEOINFORMATION ENVIRONMENT (review)

V.P. Yakushev, V.V. Yakushev, V.L. Badenko, D.A. Matveenko,
Yu.V. Chesnokov

Agrophysical Research Institute, 14, Grazhdanskii prosp., St. Petersburg, 195220 Russia, e-mail vyakushev@agrophys.ru, mail@agrophys.com, vbadenko@gmail.com, dmatveenko@inbox.ru, yuv_chesnokov@agrophys.ru (✉ corresponding author)

ORCID:
Yakushev V.P. orcid.org/0000-0002-0013-0484
Matveenko D.A. orcid.org/0000-0002-8937-8506
Yakushev V.V. orcid.org/0000-0001-8434-5580
Chesnokov Yu.V. orcid.org/0000-0002-1134-0292
Badenko V.L. orcid.org/0000-0002-3054-1786

Received December 16, 2019

 

In the context of changing socio-economic, natural and climatic conditions, there is a need for effective management tools to adapt agricultural activities. Such tools are farming systems, which are a set of interconnected agrotechnical, reclamation and organizational measures aimed at the efficient use of agrolandscapes, preservation and improvement of soil fertility, and obtaining high crop yields. The efficiency of agricultural production can be improved by using various forecasting methods based on the use of mathematical models. In crop production, statistical and dynamic simulation forecast models have been developed. The latter are more accurate and adaptive and allow you to get an answer to the question about the development of argoecosystems in the conditions of changing climatic conditions and the application of various agricultural measures. The paper provides an overview of methodological approaches for predicting crop productivity based on mass calculations of a simulation model of an agroecosystem in a geoinformation environment that can be used to justify farming systems. The analysis of the state of the problem is carried out, which presents the main current trends in the use of simulation models of agroecosystems in decision support systems for management in agriculture in general and in the support of farming systems in particular. Existing approaches and methods are classified based on spatial coverage into macro-scale, meso-scale, and micro-scale modeling methods. In the general case, these different methods require different methodological approaches are presented in the paper. The relevant basic methods and approaches for creating a universal environment for mass calculations of dynamic models of agroecosystems for different levels of spatial coverage are also presented. The analysis of the very important issue of choosing a set of control (base) points is presented where model calculations will be performed that should belong to real agricultural fields and sufficiently reflect the diversity of soil and climatic conditions of the region under consideration. Also presented are the requirements for a universal modeling environment for carrying out calculations on different models from various suppliers.

Keywords: agroecosystems, simulation modeling, mass calculations, forecasting, geographic information systems, farming systems.

 

REFERENCES

  1. Rodriguez D., de Voil P., Rufino M.C., Odendo M., Van Wijk M.T. To mulch or to munch? Big modelling of big data. Agricultural Systems, 2017, 153: 32-42 CrossRef
  2. Sierra J., Causeret F., Chopin P. A framework coupling farm typology and biophysical modelling to assess the impact of vegetable crop-based systems on soil carbon stocks. Application in the Caribbean. Agricultural Systems, 2017, 153: 172-180 CrossRef
  3. Jeuffroy M.H., Casadebaig P., Debaeke P., Loyce C., Meynard J.M. Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review. Agronomy for Sustainable Development, 2014, 34(1): 121-137. CrossRef
  4. Dury J., Schaller N., Garcia F., Reynaud A., Bergez J. E. Models to support cropping plan and crop rotation decisions. A review. Agronomy for Sustainable Development, 2012, 32(2): 567-580. CrossRef
  5. Jones J.W., Antle J.M., Basso B., Boote K.J., Conant R.T., Foster I., Godfray H.C.J., Herrero M., Howitt R.E., Janssen S., Keating B.A., Munoz-Carpena R., Porter C.H., Rosenzweig C., Wheeler T.R. Brief history of agricultural systems modeling. Agricultural Systems, 2017, 155: 240-254 CrossRef
  6. Morais R., Silva N., Mendes J., Adão T., Pádua L., López-Riquelme J.A., Pavón-Pulido N., Sousa J.J., Peres E. Mysense: A comprehensive data management environment to improve precision agriculture practices. Computers and Electronics in Agriculture, 2019, 162: 882-894 CrossRef
  7. Insua J.R., Utsumi S.A., Basso B. Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models. PloS ONE, 2019, 14(3): e0212773 CrossRef
  8. Gebremedhin A., Badenhorst P.E., Wang J., Spangenberg G.C., Smith K.F. Prospects for measurement of dry matter yield in forage breeding programs using sensor technologies. Agronomy, 2019, 9(2): 65 CrossRef
  9. Reynolds D., Ball J., Bauer A., Davey R., Griffiths S., Zhou J. CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. GigaScience, 2019, 8(3): giz009 CrossRef
  10. Hunt E.R. Jr., Daughtry C.S. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing, 2018, 39(15-16): 5345-5376 CrossRef
  11. Farooque A.A., Chang Y.K., Zaman Q.U., Groulx D., Schuman A.W., Esau T.J. Performance evaluation of multiple ground-based sensors mounted on a commercial wild blueberry harvester to sense plant height, fruit yield and topographic features in real-time. Computers and Electronics in Agriculture, 2013, 91: 135-144 CrossRef
  12. Sankaran S., Khot L.R., Espinoza C.Z., Jarolmasjed S., Sathuvalli V.R., Vandemark G.J., Miklas P.N., Carter A.H., Pumphrey M.O., Knowles N.R.N., Pavek M.J. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 2015, 70: 112-123 CrossRef
  13. Mirschel W., Wieland R., Wenkel K.O., Nendel C., Guddat C. YIELDSTAT — a spatial yield model for agricultural crops. European Journal of Agronomy, 2014, 52: 33-46 CrossRef
  14. Kern A., Barcza Z., Marjanović H., Árendás T., Fodor N., Bónis P., Bognár P., Lichtenberger J. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agricultural and Forest Meteorology, 2018, 260: 300-320 CrossRef
  15. Conradt T., Gornott C., Wechsung F. Extending and improving regionalized winter wheat and silage maize yield regression models for Germany: enhancing the predictive skill by panel definition through cluster analysis. Agricultural and Forest Meteorology, 2016, 216: 68-81 CrossRef
  16. Gutzler C., Helming K., Balla D., Dannowski R., Deumlich D., Glemnitz M., Sieber S. Agricultural land use changes — a scenario-based sustainability impact assessment for Brandenburg, Germany. Ecological Indicators, 2015, 48: 505-517 CrossRef
  17. Grados D., Schrevens E. Multidimensional analysis of environmental impacts from potato agricultural production in the Peruvian Central Andes. Science of the Total Environment, 2019, 663: 927-934 CrossRef
  18. Antle J.M., Jones J.W., Rosenzweig C. Next generation agricultural system models and knowledge products: synthesis and strategy. Agricultural Systems, 2017, 155: 179-185 CrossRef
  19. Janssen S.J., Porter C.H., Moore A.D., Athanasiadis I.N., Foster I., Jones J.W., Antle J.M. Towards a new generation of agricultural system data, models and knowledge products: information and communication technology. Agricultural Systems, 2017, 155: 200-212 CrossRef
  20. Fielke S., Taylor B., Jakku E. Digitalisation of agricultural knowledge and advice networks: a state-of-the-art review. Agricultural Systems, 2020, 180: 102763 CrossRef
  21. Lecerf R., Ceglar A., López-Lozano R., Van Der Velde M., Baruth B. Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe. Agricultural Systems, 2019, 168: 191-202 CrossRef
  22. Ozturk I., Sharif B., Baby S., Jabloun M., Olesen J.E. The long-term effect of climate change on productivity of winter wheat in Denmark: a scenario analysis using three crop models. The Journal of Agricultural Science, 2017, 155(5): 733-750 CrossRef
  23. Hannah L., Donatti C.I., Harvey C.A., Alfaro E., Rodriguez D.A., Bouroncle C., Castellanos E., Diaz F., Fung E., Hidalgo H.G., Imbach P. Regional modeling of climate change impacts on smallholder agriculture and ecosystems in Central America. Climatic Change, 2017, 141(1): 29-45 CrossRef
  24. Belem M., Saqalli M. Development of an integrated generic model for multi-scale assessment of the impacts of agro-ecosystems on major ecosystem services in West Africa. Journal of Environmental Management, 2017, 202: 117-125 CrossRef
  25. Badenko V.L., Topaj A.G., Yakushev V.V., Mirschel W., Nendel C. Crop models as research and interpretative tools. Sel'skokhozyaistvennaya biologiya [Agricultural Biology], 2017, 52: 437-445 CrossRef
  26. Anten N.P., Vermeulen P.J. Tragedies and crops: understanding natural selection to improve cropping systems. Trends in Ecology & Evolution, 2016, 31(6): 429-439 CrossRef
  27. Lee H., Lautenbach S., Nieto A.P.G., Bondeau A., Cramer W., Geijzendorffer I.R. The impact of conservation farming practices on Mediterranean agro-ecosystem services provisioning — a meta-analysis. Regional Environmental Change, 2019, 19: 2187-2202 CrossRef
  28. Poluektov R.A., Fintushal S.M., Oparina I.V., Shatskikh D.V., Terleev V.V., Zakharova E.T. AGROTOOL — a system for crop simulation. Archives of Agronomy and Soil Science, 2002, 48(6): 609-635 CrossRef
  29. Badenko V.L., Terleev V.V., Topaj A. G. AGROTOOL software as an intellectual core of decision support systems in computer aided agriculture. Applied Mechanics and Materials, 2014, 635-637: 1688-1691 CrossRef
  30. de Wit C.T., Van Keulen H. Modelling production of field crops and its requirements. Geoderma, 1987, 40(3-4): 253-265 CrossRef
  31. Nendel C., Kersebaum K.C., Mirschel W., Wenkel K.O. Testing farm management options as climate change adaptation strategies using the MONICA model. European Journal of Agronomy, 2014, 52: 47-56 CrossRef
  32. Medvedev S., Topaj A. Crop simulation model registrator and polyvariant analysis. IFIP Advances in Information and Communication Technology, 2011, 359: 295-301 CrossRef
  33. Ramírez-Cuesta J.M., Mirás-Avalos J.M., Rubio-Asensio J.S., Intrigliolo D.S. A novel ArcGIS toolbox for estimating crop water demands by integrating the dual crop coefficient approach with multi-satellite imagery. Water, 2019, 11(1): 38 (doi: 10.3390/w11010038)
  34. Liben F.M., Wortmann C.S., Tirfessa A. Geospatial modeling of conservation tillage and nitrogen timing effects on yield and soil properties. Agricultural Systems, 2020, 177: 102720 CrossRef
  35. Liben F.M., Wortmann C.S., Yang H., Lindquist J.L., Tadesse T., Wegary D. Crop model and weather data generation evaluation for conservation agriculture in Ethiopia. Field Crops Research, 2018, 228: 122-134 CrossRef
  36. Hartkamp A.D., White J.W., Hoogenboom G. Interfacing geographic information systems with agronomic modeling: a review. Agronomy Journal, 1999, 91(5): 761-772 CrossRef
  37. Shelia V., Hansen J., Sharda V., Porter C., Aggarwal P., Wilkerson C.J., Hoogenboom G. A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies. Environmental Modelling & Software, 2019, 115: 144-154 CrossRef
  38. Bassoa B., Liua L. Seasonal crop yield forecast: methods, applications, and accuracies. Advances in Agronomy, 2018, 154: 201-255 CrossRef
  39. Huang J., Gómez-Dans J., Huang H., Ma H., Wu Q., Lewis P., Liang S., Chen Z., Xue J., Wu Y., Zhao F., Wang J., Xie X. Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 2019, 276: 107-109 CrossRef
  40. Machwitz M., Hass E., Junk J., Udelhoven T., Schlerf M. Crop GIS — a web application for the spatial and temporal visualization of past, present and future crop biomass development. Computers and Electronics in Agriculture, 2019, 161: 185-193 CrossRef
  41. Jin X., Kumar L., Li Z., Feng H., Xu X., Yang G., Wang, J. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 2018, 92: 141-152 CrossRef
  42. Badenko V.L., Topazh A.G., Medvedev S.A., Zakharova E.T. AgroEkoInfo, 2018, 3(33): 68 (in Russ.).
  43. Reynolds M., Kropff M., Crossa J., Koo J., Kruseman G., Molero Milan A., Rutkoski J., Schulthess U., Balwinder-Singh, Sonder K., Tonnang H., Vadez V. Role of modelling in international crop research: overview and some case studies. Agronomy, 2018, 8(12): 291 CrossRef
  44. Kumhálová J., Matějková Š. Yield variability prediction by remote sensing sensors with different spatial resolution. International Agrophysics, 2017, 31(2): 195-202 CrossRef
  45. Resop J.P., Fleisher D.H., Wang Q., Timlin D.J., Reddy V.R. Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units. Computers and Electronics in Agriculture, 2012, 89: 51-61 CrossRef
  46. Hodson D., White J. GIS and crop simulation modelling applications in climate change research. In: Climate change and crop production. CABI Publishers,Wallingford, UK, 2010: 245-262 CrossRef
  47. Akpoti K., Kabo-bah A.T., Zwart S.J. Agricultural land suitability analysis: state-of-the-art and outlooks for integration of climate change analysis. Agricultural Systems, 2019, 173: 172-208 CrossRef
  48. Mirschel W., Schultz A., Wenkel K.O., Wieland R., Poluektov R.A. Crop growth modelling on different spatial scales — a wide spectrum of approaches. Archives of Agronomy and Soil Science, 2004, 50(3): 329-343 CrossRef
  49. Gaso D.V., Berger A.G., Ciganda V.S. Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images. Computers and Electronics in Agriculture, 2019, 159: 75-83 CrossRef
  50. Maharjan G.R., Hoffmann H., Webber H., Srivastava A.K., Weihermüller L., Villa A., Coucheney E., Lewan E., Trombi G., Moriondo M., Bindi M., Grosz B., Dechow R., Kuhnert M., Doro L., Kersebaum K.-C., Stella T., Specka X., Nendel C., Constantin J., Raynal H., Ewert F., Gaiser T. Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates. European Journal of Agronomy, 2019, 103: 32-46 CrossRef
  51. Wenkel K.O., Berg M., Mirschel W., Wieland R., Nendel C., Köstner B. LandCaRe DSS — an interactive decision support system for climate change impact assessment and the analysis of potential agricultural land use adaptation strategies. Journal of Environmental Management, 2013, 127: 168-183 CrossRef
  52. Zhai Z., Martínez J.F., Beltran V., Martínez N.L. Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 2020, 170: 105256 CrossRef
  53. Battisti R., Parker P.S., Sentelhas P. C., Nendel C. Gauging the sources of uncertainty in soybean yield simulations using the MONICA model. Agricultural Systems, 2017, 155: 9-18 CrossRef
  54. Holzworth D.P., Snow V., Janssen S., Athanasiadis I.N., Donatelli M., Hoogenboom G., White J.W., Thorburn P. Agricultural production systems modelling and software: current status and future prospects. Environmental Modelling & Software, 2015, 72: 276-286 CrossRef
  55. Yin Y., Zhang X., Lin D., Yu H., Shi P. GEPIC-VR model: a GIS-based tool for regional crop drought risk assessment. Agricultural Water Management, 2014, 144: 107-119 CrossRef
  56. Donatelli M., Srivastava A.K., Duveiller G., Niemeyer S., Fumagalli D. Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe. Environmental Research Letters, 2015, 10(7): 075005 CrossRef
  57. Seguini L., Bussay A., Baruth B. From extreme weather to impacts: the role of the areas of concern maps in the JRC MARS bulletin. Agricultural Systems, 2019, 168: 213-223 CrossRef
  58. Thorp K.R., Bronson K.F. A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations. Environmental Modelling & Software, 2013, 50: 25-36 CrossRef
  59. Elliott J., Kelly D., Chrryssanthacopoulos J., Glotter M., Jhunjhnuwala K., Best N., Wilde M. Foster I. The parallel system for integrating impact models and sectors (pSIMS). Environmental Modelling & Software, 2014, 62: 509-516. CrossRef
  60. Gabaldón-Leal C., Webber H., Otegui M.E., Slafer G.A., Ordóñez R.A., Gaiser T., Lorite I.J., Ruiz-Ramos M., Ewert F. Modelling the impact of heat stress on maize yield formation. Field Crops Research, 2016, 198: 226-237 CrossRef
  61. Huang J., Scherer L., Lan K., Chen F., Thorp K.R. Advancing the application of a model-independent open-source geospatial tool for national-scale spatiotemporal simulations. Environmental Modelling & Software, 2019, 119: 374-378 CrossRef
  62. Calera A., Campos I., Osann A., D’Urso G., Menenti M. Remote sensing for crop water management: from ET modelling to services for the end users. Sensors, 2017, 17(5): 1104 CrossRef
  63. Suchkov A.P. Sistemy i sredstva informatiki, 2015, 25(3): 195-205 CrossRef (in Russ.).
  64. Topazh A.G., Mitrofanov E.P. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaya matematika. Informatika. Protsessyupravleniya, 2017, 13(3): 326-338 CrossRef (in Russ.).
  65. de Wit A., Boogaard H., Fumagalli D., Janssen S., Knapen R., van Kraalingen D., Supit I., van der Wijngaart R., van Diepen K. 25 years of the WOFOST cropping systems model. Agricultural Systems, 2019, 168: 154-167 CrossRef
  66. Ewert F., Rötter R.P., Bindi M., Webber H., Trnka M., Kersebaum K.C., Olesen J.E., van Ittersum M.K., Janssen S., Rivington M., Semenov M.A., Wallach D., Porter J.R., Stewart D., Verhagen J., Gaiser T., Palosuo T., Tao F., Nendel C., Roggero P.P., Bartosová L., Asseng S. Crop modelling for integrated assessment of risk to food production from climate change. Environmental Modelling & Software, 2015, 72: 287-303 CrossRef
  67. Asseng S., Ewert F., Rosenzweig C., Jones J.W., Hatfield J.L., Ruane A.C., Boote K.J., Thorburn P.J., Rötter R.P., Cammarano D., Brisson N., Basso B., Martre P., Aggarwal P.K., Angulo C., Bertuzzi P., Biernath C., Challinor A.J., Doltra J., Gayler S., Goldberg R., Grant R., Heng L., Hooker J., Hunt L.A., Ingwersen J., Izaurralde R.C., Kersebaum K.C., Müller C., Naresh Kumar S., Nendel C., O’Leary G., Olesen J.E., Osborne T.M., Palosuo T., Priesack E., Ripoche D., Semenov M.A., Shcherbak I., Steduto P., Stöckle C., Stratonovitch P., Streck T., Supit I., Tao F., Travasso M., Waha K., Wallach D., White J.W., Williams J.R., Wolf J. Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 2013, 3(9): 827-832 CrossRef
  68. Jones J.W., Hoogenboom G., Porter C.H., Boote K.J., Batchelor W.D., Hunt L.A., Wilkens P.W., Singh U., Gijsman A.J., Ritchie J.T. The DSSAT cropping system model. European Journal of Agronomy, 2003, 18(3-4): 235-265 CrossRef
  69. Wang X., Williams J.R., Gassman P.W., Baffaut C., Izaurralde R.C., Jeong J., Kiniry J.R. EPIC and APEX: model use, calibration, and validation. Transactions of the ASABE, 2012, 55(4): 1447-1462 CrossRef
  70. Brisson N., Gary C., Justes E., Roche R., Mary B., Ripoche D., Zimmer D., Sierraa J., Bertuzzi P., Burgera P., Bussière F., Cabidoche Y.M., Cellier P., Debaeke P., Gaudillère J.P., Hénault C., Maraux F., Seguin B., Sinoquet H. An overview of the crop model STICS. European Journal of Agronomy, 2003, 18(3-4): 309-332 CrossRef
  71. Leghari S.J., Hu K., Liang H., Wei Y. Modeling water and nitrogen balance of different cropping systems in the North China Plain. Agronomy, 2019, 9(11): 696 CrossRef
  72. Jiang R., He W., Zhou W., Hou Y., Yang J.Y., He P. Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models. Computers and Electronics in Agriculture, 2019, 166: 104-988 CrossRef
  73. Xiang Z., Bailey R.T., Nozari S., Husain Z., Kisekka I., Sharda V., Gowda P. DSSAT-MODFLOW: a new modeling framework for exploring groundwater conservation strategies in irrigated areas. Agricultural Water Management, 2020, 232: 106033 CrossRef
  74. Newbery F., Qi A., Fitt B.D. Modelling impacts of climate change on arable crop diseases: progress, challenges and applications. Current Opinion in Plant Biology, 2016, 32: 101-109 CrossRef
  75. Badenko V.L., Garmanov V.V., Ivanov D.A., Savchenko A.N., Topazh A.G. Doklady Rossiiskoi akademii sel'skokhozyaistvennykh nauk, 2015, 1-2: 72-76 (in Russ.).
  76. Badenko V.L., Badenko G., Topaj A.G., Medvedev S., Zakharova E., Terleev V.V. Comparative simulation of various agricultural land use practices for analysis of impacts on environments. Environments, 2017, 4(4): 92 CrossRef
  77. Engel T., Hoogenboom G., Jones J.W., Wilkens P.W. AEGIS/WIN: a computer program for the application of crop simulation models across geographic areas. Agronomy Journal, 1997, 89(6): 919-928 CrossRef
  78. McDonald C.K., MacLeod N.D., Lisson S., Corfield J.P. The Integrated Analysis Tool (IAT) — a model for the evaluation of crop-livestock and socio-economic interventions in smallholder farming systems. Agricultural Systems, 2019, 176: 102659 CrossRef
  79. Zhao G., Bryan B.A., King D., Luo Z., Wang E., Bende-Michl U., Song X., Yu Q. Large-scale, high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing. Environmental Modelling & Software, 2013, 41: 231-238 CrossRef
  80. Badenko V.L., Topazh A.G., Medvedev S.A., Zakharova E.T., Dunaeva E.A. Tavricheskii vestnik agrarnoi nauki, 2019, 3: 18-30 CrossRef (in Russ.).
  81. Aref'ev N.V., Badenko V.L., Latyshev, N.K. Nauchno-tekhnicheskie vedomosti SPbPU. Estestvennye i inzhenernye nauki, 2010, 110: 205-210 (in Russ.).
  82. Medvedev S.A., Poluektov R.A., Topazh A.G. Melioratsiya i vodnoe khozyaistvo, 2012, 2: 10-13 (in Russ.).
  83. Topaj A., Badenko V., Medvedev S., Terleev V. V knige: Novye metody i rezul'taty issledovanii landshaftov v Evrope, Tsentral'noi Azii i Sibiri. Tom III. Monitoring i modelirovanie landshaftov /Pod redaktsiei V.G. Sycheva, L. Myullera [In: New methods and results of landscape studies in Europe, Central Asia and Siberia. Vol. III. Landscape monitoring and modeling. V.G. Sychev, L. Myuller (eds.)]. Moscow, 2018: 253-257 CrossRef (in Russ.).
  84. Poluektov R.A., Oparina I.V., Topazh A.G., Mirshel' V. Matematicheskoe modelirovanie, 2000, 12(11): 3-16 (in Russ.).
  85. Dobor L., Barcza Z., Hlásny T., Árendás T., Spitkó T., Fodor N. Crop planting date matters: Estimation methods and effect on future yields. Agricultural and Forest Meteorology, 2016, 223: 103-115 CrossRef
  86. Rötter R.P., Hoffmann M.P., Koch M., Müller C. Progress in modelling agricultural impacts of and adaptations to climate change. Current Opinion in Plant Biology, 2018, 45: 255-261 CrossRef
  87. Bannayan M., Hoogenboom G. Weather analogue: a tool for real-time prediction of daily weather data realizations based on a modified k-nearest neighbor approach. Environmental Modelling & Software, 2008, 23(6): 703-713 CrossRef
  88. Semenov M.A. Using weather generators in crop modelling. Acta Horticulturae, 2006, 707: 93-100 CrossRef
  89. Peleg N., Fatichi S., Paschalis A., Molnar P., Burlando P. An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables. Journal of Advances in Modeling Earth Systems, 2017, 9(3): 1595-1627 CrossRef
  90. Maraun D., Huth R., Gutiérrez J.M., Martín D.S., Dubrovsky M., Fischer A., Widmann M. The VALUE perfect predictor experiment: evaluation of temporal variability. International Journal of Climatology, 2019, 39(9): 3786-3818 CrossRef
  91. Medvedev S., Topaj A., Badenko V., Terleev V. Medium-term analysis of agroecosystem sustainability under different land use practices by means of dynamic crop simulation. IFIP Advances in Information and Communication Technology, 2015, 448: 252-261 CrossRef
  92. Dunaieva I., Mirschel W., Popovych V., Pashtetsky V., Golovastova E., Vecherkov V., Melnichuk A., Terleev V., Nikonorov A., Ginevsky R., Topaj A., Lazarev V. GIS services for agriculture monitoring and forecasting: development concept. Advances in Intelligent Systems and Computing, 2019, 983: 236-246 CrossRef
  93. Teixeira E.I., de Ruiter J., Ausseil A.G., Daigneault A., Johnstone P., Holmes A., Tait A., Ewert F. Adapting crop rotations to climate change in regional impact modelling assessments. Science of the Total Environment, 2018, 616: 785-795 CrossRef
  94. Tayyebi A., Tayyebi A., Vaz E., Arsanjani J.J., Helbich M. Analyzing crop change scenario with the SmartScape™ spatial decision support system. Land Use Policy, 2016, 51: 41-53 CrossRef
  95. Palosuo T., Kersebaum K.C., Angulo C., Hlavinka P., Moriondo M., Olesen J.E., Patil R.H., Ruget F., Rumbaur C., Takáč J., Trnka M., Bindi M., Caldag B., Ewert F., Ferrise R., Mirschel W., Saylan L., Šiška B., Rötter R. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. European Journal of Agronomy,2011, 35(3): 103-114 CrossRef
  96. Wallach D., Mearns L.O., Ruane A.C., Rötter R.P., Asseng S. Lessons from climate modeling on the design and use of ensembles for crop modeling. Climatic Change, 2016, 139(3-4): 551-564 CrossRef
  97. Kollas C., Kersebaum K.C., Nendel C., Manevski K., Müller C., Palosuo T., Conradt T. Crop rotation modelling — a European model intercomparison. European Journal of Agronomy, 2015, 70: 98-111 CrossRef
  98. Raza A., Razzaq A., Mehmood S.S., Zou X., Zhang X., Lv Y., Xu J. Impact of climate change on crops adaptation and strategies to tackle its outcome: a review. Plants, 2019, 8(2): 34 CrossRef
  99. Droutsas I., Challinor A.J., Swiderski M., Semenov M.A. New modelling technique for improving crop model performance — application to the GLAM model. Environmental Modelling & Software, 2019, 118: 187-200 CrossRef

 

back

 


CONTENTS

 

 

Full article PDF (Rus)

Full article PDF (Eng)