Pavlo Lykhovyd
Institute of Climate Smart Agriculture of NAAS, Ukraine
Keywords: vegetation index, global warming, remote sensing, reference evapoytranspiration, irrigation, afforestation, mathematical modeling, crop forecasting, precision agriculture, climate smart agriculture


The work is dedicated to theoretical study of current climatic trends in Ukraine and their influence on the agricultural sector of the country’s economy together with modern opportunities of crop production increase and efficiency enhancement through the implementation of rational land use policies and the last achievements of information technologies. Robust analysis of meteorological changes and concurrent shifts in crop water requirements through the long-term periods for sustainable high-quality food production is provided. Mathematical models for reference evapotranspiration assessment are developed and introduced for Ukrainian crop producers to enhance the efficiency of agricultural water management under simultaneous stabilization of crop gross production in the conditions of increasing aridity and freshwater deficit. Revision of modern agricultural practices in the field of forest resources management, agrochemicals and pesticides use, land management, etc., is provided in the context of resource-saving and provision of environmentally friendly crop production in the country. Last achievements of remote sensing technologies are integrated in the form of mathematical models for environmental monitoring, crop growth observation and yield forecasting on local and regional scales. Recommendations on agricultural sector policies reformation and implementation of modern information technologies in Ukrainian agriculture are proposed. The monograph is directed to agricultural specialists and scientists, as well as the students who are getting higher education in the field of agronomy, crop science, irrigated agriculture, and precision agriculture.

Internal Reviewers:
Dr Liubov Boiarkina, Institute of Climate Smart Agriculture of NAAS, Ukraine
Dr Yevhenii Domaratskiy, Kherson State Agrarian and Economic University, Ukraine

External Reviewer:

Stoyanets N. V., Doctor of Economics Sciences, Professor Sumy National Agricultural University, Ukraine


Abdalla, M., Osborne, B., Lanigan, G., Forristal, D., Williams, M., Smith, P., & Jones, M. B. (2013). Conservation tillage systems: a review of its consequences for greenhouse gas emissions. Soil Use and Management, 29(2), 199-209.

Abdi, H. (2010). Coefficient of variation. Encyclopedia of research design, 1, 169-171.

Adger, W. N., & Brown, K. (1994). Land use and the causes of global warming. John Wiley & Sons.

Ahearn, M. C., Korb, P., & Banker, D. (2005). Industrialization and contracting in U.S. agriculture. Journal of Agricultural and Applied Economics, 37(2), 347-364.

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.

Akerlof, K., Maibach, E. W., Fitzgerald, D., Cedeno, A. Y., & Neuman, A. (2013). Do people “personally experience” global warming, and if so how, and does it matter?. Global Environmental Change, 23(1), 81-91.

Akmarov, P., Gorbyshina, N., & Kniazeva, O. (2019). Special aspects of digital transformation in agriculture sector of economy. Advances in Intelligent Systems Research, 167, 22-26.

Akumaga, U., & Alderman, P. D. (2019). Comparison of Penman–Monteith and Priestley-Taylor evapotranspiration methods for crop modeling in Oklahoma. Agronomy Journal, 111(3), 1171-1180.

Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27, 17-21.

Areshkina, L. (2011). Vegetation in recognition of changes in earth remote sensing images. Journal of Research and Applications in Agricultural Engineering, 56(3), 7-14.

Armstrong, J. S. (1985). Long-range Forecasting: From Crystal Ball to Computer. New York, Wiley. 348 pp.

Arnell, N. W., & Liv, C. (2001). Hydrology and Water Resources. Cambridge University Press.

Arnell, N. W., Lowe, J. A., Challinor, A. J. & Osborn, T. J. (2019). Global and regional impacts of climate change at different levels of global temperature increase. Climatic Change, 155(3), 377-391.

Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949-981.

Báez-González, A. D., Chen, P. Y., Tiscareño-López, M., & Srinivasan, R. (2002). Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop Science, 42, 1943-1949.

Bain, C., Selfa, T., Dandachi, T., & Velardi, S. (2017). ‘Superweeds’ or ‘survivors’? Framing the problem of glyphosate resistant weeds and genetically engineered crops. Journal of Rural Studies, 51, 211-221.

Bala, G., Caldeira, K., Wickett, M., Phillips, T. J., Lobell, D.B., Delire, C. & Mirin, A. (2007). Combined climate and carbon-cycle effects of large-scale deforestation. Proceedings of the National Academy of Sciences, 104(16), 6550-6555.

Barde, M. P., & Barde, P. J. (2012). What to use to express the variability of data: Standard deviation or standard error of mean?. Perspectives in clinical research, 3(3), 113.

Bartalev, S. A., & Zakora, A. L. (2019). Recognition of agricultural lands based on measurements of a vegetation index. Journal of Rocket-Space Technology, 27(4), 3-8.

Bas Cerdá, M. D. C., Ortiz Moragón, J., Ballesteros Pascual, L., & Martorell Alsina, S. S. (2017). Evaluation of a multiple linear regression model and SARIMA model in forecasting 7Be air concentrations. Chemosphere, 177, 326-333.

Bauerkämper, A. (2004). The industrialization of agriculture and its consequences for the natural environment: an inter-German comparative perspective. Historical Social Research, 29(3), 124-149.

Bayer, C., Gomes, J., Vieira, F. C. B., Zanatta, J. A., de Cássia Piccolo, M., & Dieckow, J. (2012). Methane emission from soil under long-term no-till cropping systems. Soil and Tillage Research, 124, 1-7.

Belsley, D. A., Kuh, E., & Welsch, R. E. (2005). Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley & Sons.

Bernstein, L., Bosch, P., Canziani, O., Chen, Z., Christ, R., & Riahi, K. (2008). IPCC, 2007: climate change 2007: synthesis report. Geneva, Switzerland.

Betts, R. A., Collins, M., Hemming, D. L., Jones, C. D., Lowe, J. A., & Sanderson, M. G. (2011). When could global warming reach 4° C?. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1934), 67-84.

Björn, L. O. (1996). Effects of ozone depletion and increased UV‐B on terrestrial ecosystems. International Journal of Environmental Studies, 51(3), 217-243.

Bland, J. M., & Altman, D. G. (1996). Statistics notes: measurement error. Bmj, 312(7047), 1654.

Blaney, H. F., & Griddle, W. D. (1950). Determining water requirements in irrigated areas from climatological and irrigation data. US Department of Agriculture, Soil Conservation Service, Technical Paper 96. 48 pp.

Blöschl, G., & Montanari, A. (2010). Climate change impacts—throwing the dice?. Hydrological Processes: An International Journal, 24(3), 374-381.

Boehlje, M. (1996). Industrialization of agriculture. What are the implications? CHOICES, First Quarter, 30-33.

Boehlje, M., & Gray, A. (2009). Biological manufacturing and industrialization of production agriculture. In: Working Paper No. 09-12. Purdue University.

Bolaji, B. O., & Huan, Z. (2013). Ozone depletion and global warming: Case for the use of natural refrigerant – a review. Renewable and Sustainable Energy Reviews, 18, 49-54.

Bolton, D. K., & Friedl, M. A. (2013). Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173, 74-84.

Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359-387.

Broecker, W. S., & Denton, G. H. (1990). What drives glacial cycles?. Scientific American, 262(1), 48-43.

Brookes, G., & Barfoot, P. (2006). GM crops: the first ten years-global socio-economic and environmental impacts. PG Economics Limited.

Brookes, G., & Barfoot, P. (2018). Environmental impacts of genetically modified (GM) crop use 1996-2016: impacts on pesticide use and carbon emissions. GM crops & food, 9(3), 109-139.

Budyko, M. I. (1958). The Heat Balance of the Earth's Surface. US Department of Commerce, Washington, D.D. 259 pp.

Burliai, A., Nesterchuk, Yu., Nepochatenko, O. & Naherniuk, D. (2020). Ecological Consequences of the Digitization of Agriculture. International Journal of Advanced Science and Technology, 29(8), 2329-2336.

Busse, M. R., Pope, D. G., Pope, J. C., & Silva-Risso, J. (2015). The psychological effect of weather on car purchases. The Quarterly Journal of Economics, 130(1), 371-414.

Calera, A., Martínez, C., & Melia, J. (2001). A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. International Journal of Remote Sensing, 22(17), 3357-3362.

Cannell, M.G.R. (1998). UK conifer forests may be growing faster in response to increased N deposition, atmospheric CO2 and temperature. Forestry, 71, 277-296.

Carpenter, J. E. (2010). Peer-reviewed surveys indicate positive impact of commercialized GM crops. Nature Biotechnology, 28(4), 319-321.

Castle, E. N. (1990). Agricultural industrialization in the American countryside. Greenbelt, Maryland. 53 pp.

Chang, P. (1949). Agriculture and industrialization. The adjustments that take place as an agricultural country is industrialized. Harvard University Press, Cambridge-Massachusetts. 270 pp.

Chang, T. Y., Huang, W., & Wang, Y. (2018). Something in the air: Pollution and the demand for health insurance. The Review of Economic Studies, 85(3), 1609-1634.

Chang, Y. W., Hsieh, C. J., Chang, K. W., Ringgaard, M., & Lin, C. J. (2010). Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning Research, 11(4), 1471-1490.

Chatskikh, D., & Olesen, J. E. (2007). Soil tillage enhanced CO2 and N2O emissions from loamy sand soil under spring barley. Soil and Tillage Research, 97(1), 5-18.

Chen, P., Niu, A., Liu, D., Jiang, W., & Ma, B. (2018). Time series forecasting of temperatures using SARIMA: An example from Nanjing. IOP Conference Series: Materials Science and Engineering, 394(5), 052024.

Choi, D., Gao, Z., & Jiang, W. (2020). Attention to global warming. The Review of Financial Studies, 33(3), 1112-1145.

Chung, Y. S., Choi, S. C., Silva, R. R., Kang, J. W., Eom, J. H., & Kim, C. (2017). Case study: estimation of sorghum biomass using digital image analysis with Canopeo. Biomass and Bioenergy, 105, 207-210.

Church, J. A., Gregory, J. M., Huybrechts, P., Kuhn, M., Lambeck, K., Nhuan, M. T., Qin, D., & Woodworth, P. L. (2001). Changes in sea level. In Climate Change 2001: The Scientific Basis, JT Houghton et al., Eds. Cambridge University Press.

Cline, W. R. (2007). Global Warming and Agriculture: Impact Estimates by Country. Center for Global Development and Peterson Institute for International Economics, Washington.

Cline, W. R. (2008). Global warming and agriculture. Finance and Development, March, 23-27.

Colantoni, A., Delfanti, L., Cossio, F., Baciotti, B., Salvati, L., Perini, L., & Lord, R. (2015). Soil aridity under climate change and implications for agriculture in Italy. Applied Mathematical Sciences, 9(50), 2467-2475.

Cook, B. I., Smerdon, J. E., Seager, R., & Coats, S. (2014). Global warming and 21st century drying. Climate Dynamics, 43(9), 2607-2627.

Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. New York, Chapman and Hall.

Crowley, T. J., & Kim, K. Y. (1999). Modeling the temperature response to forced climate change over the last six centuries. Geophysical Research Letters, 26(13), 1901-1904.

Cruff, R. W., & Thompson, T. H. (1967). A comparison of methods of estimating potential evapotranspiration from climatological data in arid and subhumid environments (No. 1839-M). USGPO. 32 pp.

Crutzen, P. J., Mosier, A. R., Smith, K. A., & Winiwarter, W. (2007). N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels. Atmospheric Chemistry and Physics Discussions, 7(4), 11191-11205.

Cubasch, U., Meehl, G. A., Boer, G. J., Stouffer, R. J., Dix, M., Noda, A., Senior, C. A., Raper, S., & Yap, K. S. (2001). Projections of future climate change. In Climate Change 2001: The scientific basis. Contribution of WG1 to the Third Assessment Report of the IPCC (TAR) (pp. 525-582). Cambridge University Press.

Dai, A. (2011). Drought under global warming: a review. Wiley Interdisciplinary Reviews: Climate Change, 2(1), 45-65.

Dale, P. J., Clarke, B., & Fontes, E. M. (2002). Potential for the environmental impact of transgenic crops. Nature Biotechnology, 20(6), 567-574.

Dalton, J. (1802). Experimental essays on the constitution of mixed gases; on the force of steam of vapour from waters and other liquids in different temperatures, both in a torricellian vacuum and in air on evaporation and on the expansion of gases by heat. Memoirs of the Manchester Literary and Philosophical Society, 5, 535-602.

De la Casa, A., Ovando, G., Bressanini, L., Martínez, J., Díaz, G., & Miranda, C. (2018). Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 531-547.

De Marco, P., & Coehlo, F. M. (2004). Services performed by the ecosystem: forest remnants influence agricultural cultures’ pollination and production. Biodiversity & Conservation, 13(7), 1245-1255.

De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38-48.

Delworth, T. L., & Knutson, T. R. (2000). Simulation of early 20th century global warming. Science, 287, 2246-2250.

Ding, M., Zhang, Y., Liu, L., Zhang, W., Wang, Z., & Bai, W. (2007). The relationship between NDVI and precipitation on the Tibetan Plateau. Journal of Geographical Sciences, 17(3), 259-268.

Dodge, Y. (2008). The Concise Encyclopedia of Statistics. Springer.

Drabenstott, M. (1995). Agricultural industrialization: implications for economic development and public policy. Journal of Agricultural and Applied Economics, 27(1), 13-20.

Eduarda, M. D. O., de Carvalho, L. M., Junior, F. W. A., & de Mello, J. M. (2007). The assessment of vegetation seasonal dynamics using multitemporal NDVI and EVI images derived from MODIS. In 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images (pp. 1-5). IEEE.

Elmadfa, I., & Meyer, A. L. (2010). Importance of food composition data to nutrition and public health. European journal of clinical nutrition, 64(3), S4-S7.

Elmore, A. J., Mustard, J. F., Manning, S. J., & Lobell, D. B. (2000). Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sensing of Environment, 73(1), 87-102.

Fang, H., Liang, S., & Hoogenboom, G. (2011). Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation. International Journal of Remote Sensing, 32, 1039-1065.

Fang, X., Pyle, J. A., Chipperfield, M. P., Daniel, J. S., Park, S., & Prinn, R. G. (2019). Challenges for the recovery of the ozone layer. Nature Geoscience 12(8), 592-596.


Farman, J. C., Gardiner, B. G., & Shanklin, J. D. (1985). Large losses of total ozone reveal seasonal ClOX/NOx interaction. Nature, 315, 207-210.

Fernández-Cirelli, A., Arumí, J. L., Rivera, D., & Boochs, P. W. (2009). Environmental effects of irrigation in arid and semi-arid regions. Chilean Journal of Agricultural Research, 69(SUPPL. 1), 27-40.

Findley, D. F. (1991). Counterexamples to parsimony and BIC. Annals of the Institute of Statistical Mathematics, 43(3), 505-514.

Florides, G. A., & Christodoulides, P. (2009). Global warming and carbon dioxide through sciences. Environment International, 35, 390-401.

Fontanet, M., Scudiero, E., Skaggs, T. H., Fernàndez-Garcia, D., Ferrer, F., Rodrigo, G., & Bellvert, J. (2020). Dynamic management zones for irrigation scheduling. Agricultural Water Management, 238, 106207.

Free, M., & Robock, A. (1999). Global warming in the context of the Little Ice Age. Journal of Geophysical Research: Atmospheres, 104(D16), 19057-19070.

Fyfe, J. C., Gillett, N. P., & Zwiers, F. W. (2013). Overestimated global warming over the past 20 years. Nature Climate Change, 3(9), 767-769.

Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Joel, G., Penuelas, J., & Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1), 28-41.

Gergonne, J. D. (1974). The application of the method of least squares to the interpolation of sequences. Historia Mathematica, 1(4), 439-447.

Glover, R. S. (1988). Farmers pay the price for advances in biotech. Atlanta Constitution, Atlanta, GA.

Gocic, M., & Trajkovic, S. (2013). Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia. Global and Planetary Change, 100, 172-182.

Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. Science, 327(5967), 812-818.

Goloborodko, S. P., & Dymov, O. M. (2019). Global climate change: causes of occurrence and consequences for agricultural production in the Southern Steppe. Land Reclamation and Water Management, 1, 88-97.

Goodwin, A. W., Lindsey, L. E., Harrison, S. K., & Paul, P. A. (2018). Estimating wheat yield with normalized difference vegetation index and fractional green canopy cover. Crop, Forage & Turfgrass Management, 4(1), 1-6.

Götz, F. P. (1951). Ozone in the atmosphere. In: Compendium of Meteorology, pp. 275-291. American Meteorological Society, Boston, MA.

Greene, W. H. (2003). Econometric analysis. Pearson Education India.

Haghverdi, A., Singh, A., Sapkota, A., Reiter, M., & Ghodsi, S. (2021). Developing irrigation water conservation strategies for hybrid bermudagrass using an evapotranspiration-based smart irrigation controller in inland southern California. Agricultural Water Management, 245, 106586.

Haider, S., & Adnan, S. (2014). Classification and assessment of aridity over Pakistan provinces (1960-2009). International Journal of Environment, 3(4), 24-35.

Hajabbasi, M. A., Jalalian, A. & Karimzadeh, H. R. (1997). Deforestation effects on soil physical and chemical properties, Lordegan, Iran. Plant and Soil, 190(2), 301-308.

Haldar, I. (2011). Global Warming: The Causes and Consequences. Readworthy.

Hamilton, N. D. (1994). Agriculture without farmers? Is industrialization restructuring American food production and threatening the future of sustainable agriculture? Northern Illinois University Law Review, 14, 613-657.

Hamon, W. R. (1961). Estimating potential evapotranspiration. Journal of the Hydraulics Division, 87(3), 107-120.

Hanbing, Z., Xiaoping, Y., & Jialin, L. (2011). MODIS data based NDVI Seasonal dynamics in agro-ecosystems of south bank Hangzhouwan bay. African Journal of Agricultural Research, 6(17), 4025-4033.

Hannan, E. J., & Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 190-195.

Hargreaves G. L., & Samani, Z. A. (1985) Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1(2), 96-99.

Harvey, D. (2000). Global Warming. The Hard Science. Pearson Education Ltd., Harlow, UK. 336 pp.

Hayami, Y. (1969). Industrialization and agricultural productivity: an international comparative study. The Developing Economies, 3-21.

Hayes, M., & Wood, D. (2012). Standardized Precipitation Index User Guide. WMO-No. 1090. Geneva. 26 pp.

Helander, M., Saloniemi, I., & Saikkonen, K. (2012). Glyphosate in northern ecosystems. Trends in Plant Science, 17(10), 569-574.

Hendrickson, M., & James, Jr., H. S. (2004). The ethics of constrained choice: how the industrialization of agriculture impacts farming and farmer behavior. Agricultural Economics, Department of Agricultural Economics Working Paper No. AEWP 2004-3. 29 pp.

Herold, M., Scepan, J., & Clarke, K. C. (2002). The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environment and Planning A, 34(8), 1443-1458.

Hinkle, D. E., Wiersma, W., & Jurs, S. G. (1998). Correlation: A measure of relationship. Applied statistics for the behavioral sciences, 4(1), 105-131.

Hinrichs, C. C., & Welsh, R. (2003). The effects of the industrialization of US livestock agriculture on promoting sustainable production practices. Agriculture and Human Values, 20, 125-141.

Hobbs, T. J. (1997). Atmospheric correction of NOAA-11 NDVI data in the arid rangelands of Central Australia. International Journal of Remote Sensing, 18(5), 1051-1058.

Hoerling, M., Eischeid, J., & Perlwitz, J. (2010). Regional precipitation trends: Distinguishing natural variability from anthropogenic forcing. Journal of Climate, 23(8), 2131-2145.

Holdridge, L. R. (1959). Simple method for determining potential evapotranspiration from temperature data. Science, 130(3375), 572-572.

Houghton, J. (2005). Global warming. Reports on Progress in Physics, 68, 1343-1403.

Houghton, R. A. (1990). The global effects of tropical deforestation. Environmental Science & Technology, 24(4), 414-422.

Houghton, R. A., Byers, B. & Nassikas, A. A. (2015). A role for tropical forests in stabilizing atmospheric CO2. Nature Climate Change, 5(12), 1022-1023.

Huber, P. (2004). Robust Statistics. Hoboken, New Jersey, John Wiley & Sons.

Huete, A., Justice, C., & Van Leeuwen, W. (1999). MODIS vegetation index (MOD13). Algorithm theoretical basis document, 3(213), 295-309.

Hughes, L. (2000). Biological consequences of global warming: is the signal already apparent?. Trends in Ecology & Evolution, 15(2), 56-61.

Hughes, T. P., Kerry, J. T., Baird, A. H., Connolly, S. R., Dietzel, A., Eakin, C. M., Heron, S. F., Hoey, A. S., Hoogenboom, M. O., Liu, G., McWilliam, M. J., Pears, R. J., Pratchett, M. S., Skirving, W. J., Stella, J. S., & Torda, G. (2018). Global warming transforms coral reef assemblages. Nature, 556(7702), 492-496.

Hunsaker, D. J., Fitzgerald, G. J., French, A. N., Clarke, T. R., Ottman, M. J., & Pinter Jr, P. J. (2007). Wheat irrigation management using multispectral crop coefficients: II. Irrigation scheduling performance, grain yield, and water use efficiency. Transactions of the ASABE, 50(6), 2035-2050.

Hunsaker, D. J., Pinter, Jr, P. J., Clarke, T. R., Fitzgerald, G. J., & French, A. N. (2006). Performance of crop coefficients inferred from NDVI observations for estimating evapotranspiration and irrigation scheduling of wheat. In World Environmental and Water Resource Congress 2006: Examining the Confluence of Environmental and Water Concerns (pp. 1-13).

Hunter, M. L., & Hunter Jr., M. L. (1999). Maintaining biodiversity in forest ecosystems. Cambridge University Press.

Hye, Q. M. A. (2009). Agriculture on the road to industrialisation and sustainable economic growth: an empirical investigation for Pakistan. International Journal of Agricultural Economics & Rural Development, 2(2), 1-6.

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.

Ikerd, J. E. (1993). The need for a systems approach to sustainable agriculture. Agriculture, Ecosystems and Environment, 46, 147-160.

Irmak, S., Irmak, A., Allen, R. G., & Jones, J. W. (2003). Solar and net radiation-based equations to estimate reference evapotranspiration in humid climates. Journal of Irrigation and Drainage Engineering ASCE, 129(5), 336-347.

Islam, S. A., & Rahman, M. M. (2015). Coastal afforestation in Bangladesh to combat climate change induced hazards. Journal of Science, Technology and Environment Informatics, 2(1), 13-25.

Ito, A., Reyer, C. P., Gädeke, A., Ciais, P., Chang, J., Chen, M., Francois, L., Forrest, M., Hickler, T., Ostberg, S., Shi, H., Thiery, W., & Tian, H. (2020). Pronounced and unavoidable impacts of low-end global warming on northern high-latitude land ecosystems. Environmental Research Letters, 15(4), 044006.

Jáuregui, J. M., Delbino, F. G., Bonvini, M. I. B., & Berhongaray, G. (2019). Determining yield of forage crops using the Canopeo mobile phone app. Journal of New Zealand Grasslands, 41-46.

Javeed, H. M. R., Iqbal, N., Ali, M., & Masood, N. (2021). Agriculture Contribution toward Global Warming. Climate Change and Plants: Biodiversity, Growth and Interactions, 1.

Jensen, M. E., & Haise, H. R. (1963). Estimation of evapotranspiration from solar radiation. Journal of Irrigation and Drainage Division, 89, 15-41.

Jensen, M. E., Burman, R. D., & Allen, R. G. (1990). Evapotranspiration and irrigation water requirements. ASCE, New York. 360 pp.

Jiang, D., Zhang, Y., & Lang, X. (2011). Vegetation feedback under future global warming. Theoretical and Applied Climatology, 106(1), 211-227.

Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833-3845.

Johnson, D. M. (2014). An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment, 141, 116-128.

Johnson, J. M., Weyers, S. L., Archer, D. W., & Barbour, N. W. (2012). Nitrous oxide, methane emission, and yield‐scaled emission from organically and conventionally managed systems. Soil Science Society of America Journal, 76(4), 1347-1357.

Jones, E. L. (1977). Environment, agriculture, and industrialization in Europe. Agricultural History, 51(3), 491-502.

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. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3-4), 235-265.

Jung, J. S., & Khoe, K. (2018). 6th industrialization of agriculture utilizing the technology of 4th industrial revolution. Journal of Convergence for Information Technology, 8(5), 211-217.

Júnior, W. M., Valeriano, T. T. B., & de Souza Rolim, G. (2019). EVAPO: A smartphone application to estimate potential evapotranspiration using cloud gridded meteorological data from NASA-POWER system. Computers and Electronics in Agriculture, 156, 187-192.

Kamal, A., Yingjie, M., & Ali, A. (2019). Significance of billion tree tsunami afforestation project and legal developments in forest sector of Pakistan. International Journal of Law and Society, 1, 157.

Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts on crop yield, crop water productivity and food security – A review. Progress in Natural Science, 19(12), 1665-1674.

Karkauskaite, P., Tagesson, T., & Fensholt, R. (2017). Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone. Remote Sensing, 9(5), 485.

Kashyap, P. S., & Panda, R. K. (2001). Evaluation of evapotranspiration estimation methods and development of crop-coefficients for potato crop in a sub-humid region. Agricultural Water Management, 50(1), 9-25.

Keeling, C. D., Chin, J. F. S., & Whorf, T. P. (1996). Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature, 382(6587), 146-149.

Kendall, M. G. (1975). Rank correlation methods. Griffin, London, UK.

Kerr, R. A. (2007). Global warming is changing the world. Science, 316, 188-190.

Khandekar, M. L., Murty, T. S., & Chittibabu, P. (2005). The global warming debate: A review of the state of science. Pure and Applied Geophysics, 162(8), 1557-1586.

Khvostikov, S. A., & Bartalev, S. A. (2018). Development of seasonal NDVI profiles references for main agricultural crops. Information Technologies in Remote Sensing of the Earth – RORSE, 55-59.

Kiehl, J. T., & Trenberth, K. E. (1997). Earth's annual global mean energy budget. Bulletin of American Meteorological Society, 78, 197-208.

Kilinc, A., Stanisstreet, M., & Boyes, E. (2008). Turkish students' ideas about global warming. International Journal of Environmental and Science Education, 3(2), 89-98.

Kim, Y., Huete, A. R., Miura, T., & Jiang, Z. (2010). Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data. Journal of Applied Remote Sensing, 4(1), 043520.

Kimmins, J. P. (2004). Forest ecology. Fishes and Forestry: Worldwide Watershed Interactions and Management, 17-43.

Klyashtorin, L. B., & Lyubushin, A. A. (2003). On the coherence between dynamics of the world fuel consumption and global temperature anomaly. Energy and Envieonment, 14(6), 773-782.

Klyashtorin, L. B., & Lyubushin, A. A. (2005). Cyclic climate changes and fish productivity. VNIRO Publishing, Moscow. 235 pp.

Konishi, S., & Kitagawa, G. (2008). Information criteria and statistical modeling. Springer.

Korkanç, S. Y. (2014). Effects of afforestation on soil organic carbon and other soil properties. Catena, 123, 62-69.

Koslowsky, D. (1993). The influence of viewing geometry on annual variations of NDVI. In Proceedings of IGARSS'93-IEEE International Geoscience and Remote Sensing Symposium (pp. 1140-1142). IEEE.

Kouadio, L., Newlands, N. K., Davidson, A., Zhang, Y., & Chipanshi, A. (2014). Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sensing, 6(10), 10193-10214.

Krongkaew, M. (1995). Contributions of agriculture to industrialization. In: Thailand’s Industrialisation and Its Consequences. Macmillan. (pp. 70-101).

Kustas, W. P., & Norman, J. M. (1996). Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrological Sciences Journal, 41(4), 495-516.

Lal, R. (2020). Integrating animal husbandry with crops and trees. Frontiers in Sustainable Food Systems, 4, 113.

Lange, M., Dechant, B., Rebmann, C., Vohland, M., Cuntz, M., & Doktor, D. (2017). Validating MODIS and sentinel-2 NDVI products at a temperate deciduous forest site using two independent ground-based sensors. Sensors, 17(8), 1855.

Lashof, D. A., & Ahuja, D. R. (1990). Relative contributions of greenhouse gas emissions to global warming. Nature, 344(6266), 529-531.

Lavrenko, N., Lavrenko, S., Revto, O., & Lykhovyd, P. (2018). Effect of tillage and humidification conditions on desalination properties of chickpea (Cicer arietinum L.). Journal of Ecological Engineering, 19(5), 70-75.

Lawrence, D. & Vandecar, K. (2015). Effects of tropical deforestation on climate and agriculture. Nature Climate Change, 5(1), 27-36.

Lee, D. K., In, J., & Lee, S. (2015). Standard deviation and standard error of the mean. Korean Journal of Anesthesiology, 68(3), 220.

Li, C., Li, H., Li, J., Lei, Y., Li, C., Manevski, K., & Shen, Y. (2019). Using NDVI percentiles to monitor real-time crop growth. Computers and Electronics in Agriculture, 162, 357-363.

Li, Y., Johnson, E. J., & Zaval, L. (2011). Local warming: Daily temperature change influences belief in global warming. Psychological Science, 22(4), 454-459.

Li, Z., Li, X., Wei, D., Xu, X., & Wang, H. (2010). An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in Northern Hebei Province, China. Procedia Environmental Sciences, 2, 964-969.

Liaghat, S., & Balasundram, S. K. (2010). A review: The role of remote sensing in precision agriculture. American Journal of Agricultural and Biological Sciences, 5(1), 50-55.

Lijun, Z., Zengxiang, Z., Tingting, D., & Xiao, W. (2008). Application of MODIS/NDVI and MODIS EVI to extracting the information of cultivated land and comparison analysis. Transactions from the Chinese Society of Agricultural Engineering, 24, 167-172.

Lillesaeter, O. (1982). Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling. Remote Sensing of Environment, 12(3), 247-254.

Lobell, D. B., & Field, C. B. (2007). Global scale climate-crop yield relationships and the impacts of recent warming. Environmental Research Letters, 2, 1-7.

Lowry, R. L., & Johnson, A. F. (1942). Consumptive use of water for agriculture. American Society of Civil Engineers Transactions, 107, 1243-1266.

Lukina, E. V., Stone, M. L., & Raun, W. R. (1999). Estimating vegetation coverage in wheat using digital images. Journal of Plant Nutrition, 22(2), 341-350.

Lykhovyd, P. (2019). Sweet maize yield structure depending on cultivation technology under the drip-irrigated conditions. Polish Journal of Natural Sciences, 34(2), 175-184.

Lykhovyd, P. V., & Lavrenko, S. O. (2017). Influence of tillage and mineral fertilizers on soil biological activity under sweet corn crops. Ukrainian Journal of Ecology, 7(4), 18-24.

Lykhovyd, P., Biliaieva, I., & Boitseniuk, K. (2020). The use of EVAPO mobile app for evapotranspiration assessment. The XIth International scientific and practical conference «Academic research in multidisciplinary innovation», 15-16.

Lykhovyd, P., Dementiieva, O., Lavrenko, S., & Lavrenko, N. (2019). Agro-Environmental evaluation of irrigation water from different sources, together with drainage and escape water of rice irrigation systems, according to its Impact on maize (Zea mays L.). Journal of Ecological Engineering, 20(2), 1-7.

Lyon, J. G. (2016). Remote Sensing Estimation of Crop Biophysical Characteristics at Various Scales. In: Hyperspectral Remote Sensing of Vegetation (pp. 365-396). CRC Press.

Maas, S. J. (1988). Use of remotely-sensed information in agricultural crop growth models. Ecological Modelling, 41(3-4), 247-268.

Magney, T. S., Eitel, J. U., Huggins, D. R., & Vierling, L. A. (2016). Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality. Agricultural and Forest Meteorology, 217, 46-60.

Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245-259.

Mao, X., Zhang, Y., & Shen, Y. J. (2003). Analysis dynamics and influence elements of winter wheat normalized difference vegetation index in mountain-foot plain. Chinese Journal of Eco-Agriculture, 11(2), 36-37.

Maresma, A., Chamberlain, L., Tagarakis, A., Kharel, T., Godwin, G., Czymmek, K. J., Shields, E., & Ketterings, Q. M. (2020). Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing. Computers and Electronics in Agriculture, 169, 105236.

Marlon, J., Howe, P., Mildenberger, M., & Leiserowitz, A. (2016). Yale climate opinion maps U.S. 2016.

Marsett, R. C., Qi, J., Heilman, P., Biedenbender, S. H., Watson, M. C., Amer, S., Weltz, M., Goodrich, D., & Marsett, R. (2006). Remote sensing for grassland management in the arid southwest. Rangeland Ecology & Management, 59(5), 530-540.

Martínez-López, J., Carreño, M. F., Palazón-Ferrando, J. A., Martínez-Fernández, J., & Esteve, M. A. (2014). Remote sensing of plant communities as a tool for assessing the condition of semiarid Mediterranean saline wetlands in agricultural catchments. International Journal of Applied Earth Observation and Geoinformation, 26, 193-204.

Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7(11), 2636-2651.

Mayer, S. J. (1992). Stratospheric ozone depletion and animal health. Veterinary Record, 131(6), 120-122.

McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7-23.

McGlinch, G. J., Jacquemin, S. J., & Lindsey, L. E. (2021). Evaluating winter malting barley grain yield with fractional green canopy cover. Crop, Forage & Turfgrass Management, 7(1), e20079.

McGuinness J. L., & Bordne, E. F. (1972). A comparison of lysimeter derived potential evapotranspiration with computed values. Technical Bulletin 1452, Agricultural Research Service, US Department of Agriculture, Washington, DC.

McPherson, E. G. (1993). Monitoring urban forest health. Environmental Monitoring and Assessment, 26(2), 165-174.

Meyer, A. (1926). U ber einige Zusammenhange zwischen Klima und Boden in Europa. Chemie der Erde, 2, 209-347.

Miller, B., Schulze, D., Crum, J., Hopkins, D., Jelinski, N., Malo, D., Quackenbush, P., Ransom, M., & Turk, J. (2018). Soil Explorer-Impressive Interpretations from the USA Soil Survey Maps. In EGU General Assembly Conference Abstracts (p. 10927).

Miller, G. T., & Spoolman, S. (2011). Living in the environment: principles, connections, and solutions. Cengage Learning.

Minobe, S. (1997). A 50-70 year climatic oscillation over the North Pacific and North America. Geophysical Research Letters, 24, 683-686.

Monmonier, M. (2005). Defining the wind: The Beaufort scale, and how a 19th century admiral turned science into poetry.

Morales, R. M., Miura, T., & Idol, T. (2008). An assessment of Hawaiian dry forest condition with fine resolution remote sensing. Forest Ecology and Management, 255(7), 2524-2532.

Moreno, J. J. M., Pol, A. P., Abad, A. S., & Blasco, B. C. (2013). Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4), 500-506.

Mortimore, M., Anderson, S., Cotula, L., Davies, J., Fasser, K., Hesse, C., Morton, J., Nyangena, W., Skinner, J., & Wolfangel, C. (2009). Dryland Opportunities: A New Paradigm for People, Ecosystems and Development. Gland, Switzerland. 86 pp.

Mosier, A. R., Peterson, G. A., & Sherrod, L. A. (2003, November). Mitigating net global warming potential (CO2, CH4 and N2O) in upland crop productions. In Methane and Nitrous Oxide International Workshop Proceedings (pp. 273-280).

Mudge, F. B. (1997). The development of greenhouse theory of global climate change from Victorian times. Weather, 52, 13-16.

Mukaka, M. (2012). Statistics Corner: A guide to appropriate use of. Malawi Medical Journal, 24, 69-71.

Myers, T. A., Maibach, E. W., Roser-Renouf, C., Akerlof, K., & Leiserowitz, A. A. (2013). The relationship between personal experience and belief in the reality of global warming. Nature Climate Change, 3(4), 343-347.

Myneni, R. B., Hall, F. G., Sellers, P. J., & Marshak, A. L. (1995). The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 481-486.

Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G., & Nemani, R. R. (1997). Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386(6626), 698-702.

Nagy, A., Fehér, J., & Tamás, J. (2018). Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Computers and Electronics in Agriculture, 151, 41-49.

NASA (2021). Ozone Hole Watch Website.

Neter, J., Wasserman, W. & Kutner, M. H. (1996). Applied Linear Statistical Models. Irwin, Chicago.

Neue, H. U., Wassmann, R., Lantin, R. S., Alberto, M. C., Aduna, J. B., & Javellana, A. M. (1996). Factors affecting methane emission from rice fields. Atmospheric Environment, 30(10-11), 1751-1754.

Newton, A. (2007). Forest ecology and conservation: a handbook of techniques. Oxford University Press on Demand.

Nicolopoulou-Stamati, P., Malpas, S., Kotampasi, C., Stamatis, P., & Hens, L. (2016). Chemical pesticides and human health: the urgent need for a new concept in agriculture. Frontiers in Public Health, 4, 148.

Nilsson, S., & Schopfhauser, W. (1995). The carbon-sequestration potential of a global afforestation program. Climatic Change, 30(3), 267-293.

O’Connell, M., Whitfield, D., Abuzar, M., Sheffield, K., McClymont, L., & McAllister, A. (2010). Satellite remote sensing of crop water use in perennial horticultural crops. Program and Abstracts Australian Irrigation Conference Held in Sydney, 129-130.

Oliver, M. A. (Ed.). (2010). Geostatistical applications for precision agriculture. Springer Science & Business Media.

Osadchyy, V., & Babichenko, V. (2013). The air temperature on the territory of Ukraine in today’s climate conditions. Ukrainian Geographical Journal, 4, 32-39.

Otterman, J. (1974). Baringhigh-albedosoils by over grazing: Hypo the sized desertification mechanism. Science, 186(4163), 531-533.

Page, E. S. (1954). Continuous inspection scheme. Biometrika, 41(1/2), 100-115.

Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., Blackburn, G. A., Yan, J., & Liu, J. (2015). Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. International Journal of Applied Earth Observation and Geoinformation, 34, 188-197.

Patel, J. H., & Oza, M. P. (2014). Deriving crop calendar using NDVI time-series. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 869.

Patrignani, A., & Ochsner, T. E. (2015). Canopeo: A powerful new tool for measuring fractional green canopy cover. Agronomy Journal, 107(6), 2312-2320.

Paustian, K. A. O. J. H., Andren, O., Janzen, H. H., Lal, R., Smith, P., Tian, G., Tiessen, H., Van Noordwijk, M., & Woomer, P. L. (1997). Agricultural soils as a sink to mitigate CO2 emissions. Soil Use and Management, 13, 230-244.

Pearce, D. W. (2001). The economic value of forest ecosystems. Ecosystem Health, 7(4), 284-296.

Pedhazur, E. J. (1997). Multiple regression in behavioral research (3rd ed.). Orlando, FL, Harcourt Brace.

Penman, H. C. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 193, 120-145.

Petersen, L. K. (2018). Real-time prediction of crop yields from MODIS relative vegetation health: A continent-wide analysis of Africa. Remote Sensing, 10, 1726.

Pontius, R. G., Thontteh, O., & Chen, H. (2008). Components of information for multiple resolution comparison between maps that share a real variable. Environmental and Ecological Statistics, 15(2), 111-142.

Porter, J. R., & Semenov, M. A. (2005). Crop responses to climatic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1463), 2021-2035.

Pozniak, S., & Hnatyshyn, M. (2021). Global initiative «4 per 1000» and possibilities of its implementation in Ukraine. Ukrainian Geographical Journal, 2(114), 11-19.

Prăvălie, R. (2018). Major perturbations in the Earth's forest ecosystems. Possible implications for global warming. Earth-Science Reviews, 185, 544-571.

Prentice, I. C., Farquhar, G. D., Fasham, M. J. R., Goulden, M. L., Heimann, M., Jaramillo, V. J., Kheshgi, H. S., Le Quere, C., Scholes, R. J., Wallace, D. W. R., Archer, D., Ashmore, M. R., Aumont, O., Baker, D., Battle, M., Bender, M., Bopp, L. P., Bousquet, P., Caldeira, K., Ciais, P., Cox, P. M., Cramer, W., Dentener, F., Enting, I. G., Field, C. B., Friedlingstein, P., Holland, E. A., Houghton, R. A., House, J. I., Ishida, A., Jain, A. K., Janssens, I. A., Joos, F., Kaminski, T., Keeling, C. D., Keeling, R. F., Kicklighter, D. W., Kohfeld, K. E., Knorr, W., Law, R., Lenton, T., Lindsay, K., Maier-Reimer, E., Manning, A. C., Matear, R. J., Mcguire, A. D., Melillo, J. M., Meyer, R., Mund, M., Orr, J. C., Piper, S., Plattner, K., Rayner, P. J., Sitch, S., Slater, S., Taguchi, S., Tans, P. P., Tian, H. Q., Weining, M. F., Whorf, T., & Yool, A. (2001). The Carbon Cycle and Atmospheric Carbon Dioxide. Cambridge University Press.

Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2), 81-92.

Qiu, J., Yang, J., Wang, Y., & Su, H. (2018). A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: A case study in Southern China. International Journal of Remote Sensing, 39(8), 2105-2118.

Raes, D., & Munoz, G. (2009). The ETo Calculator. Reference Manual Version 3. Food and Agriculture Organization of the United Nations, Land and Water Division: Rome, Italy. 37 pp.

Rahimikhoob, A. (2010). Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theoretical and Applied Climatology, 101(1), 83-91.

Rahman, M. R., Islam, A. H. M. H., & Rahman, M. A. (2004). NDVI derived sugarcane area identification and crop condition assessment. Plan Plus, 1(2), 1-12.

Raines, C. A. (2011). Increasing photosynthetic carbon assimilation in C3 plants to improve crop yield: current and future strategies. Plant Physiology, 155(1), 36-42.

Ramanathan, V. (2007). Global dimming by air pollution and global warming by greenhouse gases: global and regional perspectives. Nucleation and Atmospheric Aerosols, 473-483.

Reed, V., Arnall, D. B., Finch, B., & Bigatao Souza, J. L. (2021). Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC). International Journal of Agronomy, 2021, 1443191.

Rider, T. W., Vogel, J. W., Dille, J. A., Dhuyvetter, K. C. & Kastens, T. L. (2006). An economic evaluation of site-specific herbicide application. Precision Agriculture, 7(6), 379-392.

Rochette, P. (2008). No-till only increases N2O emissions in poorly-aerated soils. Soil and Tillage Research, 101(1-2), 97-100.

Rochette, P., Angers, D. A., Chantigny, M. H., & Bertrand, N. (2008). Nitrous oxide emissions respond differently to no‐till in a loam and a heavy clay soil. Soil Science Society of America Journal, 72(5), 1363-1369.

Rogan, J., & Chen, D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning, 61(4), 301-325.

Romanenko, V. A. (1961). Computation of the autumn soil moisture using a universal relationship for a large area. In: Proceedings, Ukrainian Hydrometeorological Research Institute, no. 3. Kyiv.

Rose, D. J., Agnew, C., & Miller, M. M. (1984). Reducing the problem of global warming. MIT, Cambridge, MA.

Rosenzweig, C., & Colls, J. (2005). Global warming and agriculture. In Yields of farmed species: constraints and opportunities in the 21st century. Proceedings of a University of Nottingham Easter School Series, June 2004, Sutton Bonington, UK (pp. 143-165). Nottingham University Press.

Rouse Jr, J. W., Haas, R. H., Deering, D. W., Schell, J. A., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75-10354).

Rouse Jr, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. NASA-CR-132982).

Savin, I. Y., & Negre, T. (2003). Relative time NDVI mosaics as an indicator of crop growth. Remote Sensing for Agriculture, Ecosystems, and Hydrology IV, 4879, 100-107.

Savory, A. (1989). Holistic resource management. Island Press, Covelo, CA. 564 pp.

Schlesinger, M. E., & Ramankutty, N. (1994). An oscillation in the global climate system of period 65-70 years. Nature, 367, 723-726.

Schmitz, P. K., & Kandel, H. J. (2021). Using canopy measurements to predict soybean seed yield. Remote Sensing, 13(16), 3260.

Schneider, S. A. (2011). Reconsidering the Industrialization of Agriculture. Journal of Environmental Law and Litigation, 26, 19.

Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.

Schultz, P. A., & Halpert, M. S. (1993). Global correlation of temperature, NDVI and precipitation. Advances in Space Research, 13(5), 277-280.

Schulze, D. G., Rahmani, S. R., Minai, J. O., Johnston, C. T., Fulk‐Bringman, S. S., Scott, J. R., Kong, N. N., Li, Y. S., & Mashtare Jr, M. L. (2021). Virtualizing soil science field trips. Natural Sciences Education, 50(1), e20046.

Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J., Lexer, M. J., Trotsiuk, V., Mairota, P., Svoboda, M., Fabrika, M., Nagel, T. A. & Reyer, C. P. O. (2017). Forest disturbances under climate change. Nature Climate Change, 7(6), 395-402.

Sellers, P. J. (1985). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6(8), 1335-1372.

Seo, B., Lee, J., Lee, K. D., Hong, S., & Kang, S. (2019). Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA. Field Crops Research, 238, 113-128.

Shafi, U., Mumtaz, R., García-Nieto, J., Hassan, S. A., Zaidi, S. A. R., & Iqbal, N. (2019). Precision agriculture techniques and practices: From considerations to applications. Sensors, 19(17), 3796.

Shammi, S. A., & Meng, Q. (2021). Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators, 121, 107124.

Shanmugapriya, P., Rathika, S., Ramesh, T., & Janaki, P. (2019). Applications of remote sensing in agriculture – A Review. International Journal of Current Microbiology and Applied Sciences, 8(01), 2270-2283.

Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Products. Van Nostrand, New York and MacMillan, London. 501 pp.

Sinha, R. P., Singh, S. C., & Häder, D. P. (1999). Photoecophysiology of cyanobacteria. Recent Research Developments in Photochemistry and Photobiology, 3, 91-101.

Smith, A. (1985). An inquiry into the nature and causes of the wealth of nations. Random House, New York. (pp. 3-21).

Sobrino, J. A., Julien, Y., & García-Monteiro, S. (2020). Surface temperature of the planet Earth from satellite data. Remote Sensing, 12(2), 218.

Soil Information for Environmental Modeling and Ecosystem Management. Electronic source. Access regime: (dated 10/08/2021).

Solomon, S. (2019). The discovery of the Antarctic ozone hole. Nature, 575, 46-47.

Son, S. W., Tandon, N. F., Polvani, L. M., & Waugh, D. W. (2009). Ozone hole and Southern Hemisphere climate change. Geophysical Research Letters, 36(15), L15705.

Sonka, S. (2003). Forces driving industrialization of agriculture: implications for the grain industry in the United States. In: Symposium “Product Differentiation and Market Segmentation in Grains and Oilseeds: Implications for Industry in Transition”. Washington, DC. (pp. 1-10).

Stas, M., Van Orshoven, J., Dong, Q., Heremans, S., & Zhang, B. (2016). A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT. In 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1–5). IEEE.

Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ, LEA.

Sutherst, R. W., Constable, F., Finlay, K. J., Harrington, R., Luck, J., & Zalucki, M. P. (2011). Adapting to crop pest and pathogen risks under a changing climate. Wiley Interdisciplinary Reviews: Climate Change, 2(2), 220-237.

Tabari, H., Grismer, M. E., & Trajkovic, S. (2013). Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Science, 31, 107-117.

Taylor, R. (1990). Interpretation of the correlation coefficient: a basic review. Journal of Diagnostic Medical Sonography, 6(1), 35-39.

Tenreiro, T. R., García-Vila, M., Gómez, J. A., Jiménez-Berni, J. A., & Fereres, E. (2021). Using NDVI for the assessment of canopy cover in agricultural crops within modelling research. Computers and Electronics in Agriculture, 182, 106038.

Tett, S. F., Stott, P. A., Allen, M. R., Ingram, W. J., & Mitchell, J. F. (1999). Causes of twentieth-century temperature change near the Earth's surface. Nature, 399(6736), 569-572.

Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review, 38, 55.

Tian, F., Brandt, M., Liu, Y. Y., Verger, A., Tagesson, T., Diouf, A. A., Rasmussen, K., Mbow, C., Wang, Y., & Fensholt, R. (2016). Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sensing of Environment, 177, 265-276.

Tiwari, P., & Shukla, P. (2020). Artificial neural network-based crop yield prediction using NDVI, SPI, VCI feature vectors. In Information and Communication Technology for Sustainable Development (pp. 585–594). Springer, Singapore.

Trenberth, K. E., Dai, A., van der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K., & Sheffield, J. (2014). Global warming and changes in drought. Nature Climate Change, 4, 17-22.

Trout, T. J., & Johnson, L. F. (2007). Estimating crop water use from remotely sensed NDVI, crop models, and reference ET. USCID Fourth International Conference on Irrigation and Drainage. Sacramento, California, 275-285.

Trout, T. J., Johnson, L. F., & Gartung, J. (2008). Remote sensing of canopy cover in horticultural crops. HortScience, 43(2), 333-337.

Tsakmakis, I. D., Gikas, G. D., & Sylaios, G. K. (2021). Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize. Agricultural Water Management, 255, 106998.

Tsimring, L. S. (2014). Noise in biology. Reports on Progress in Physics, 77(2), 026601.

Ulezko, A., Reimer, V., & Ulezko, O. (2019). Theoretical and methodological aspects of digitalization in agriculture. IOP Conference Series: Earth and Environmental Science, 274, 012062.

Unal, I., & Topakci, M. A. (2014). Review on using drones for precision farming applications. In: 12th International Congress on Agricultural Mechanization and Energy, Nevsehir, Turkey, 3-6 September 2014. (pp. 276-283).

Urban, T. N. (1991). Agricultural industrialization: it’s inevitable. CHOICES, Fourth Quarter, 4-6.

Uskov, A., Samsonova, N., & Zhukova, A. (2020). Satellite monitoring on grain crops: identification of problem areas and forecast of yield. Science Book Publishing House, Yelm, WA, USA. 92 pp.

Vautard, R., Gobiet, A., Sobolowski, S., Kjellström, E., Stegehuis, A., Watkiss, P., Mendlik, T., Landgren, O., Nikulin, G., Teichmann, C., & Jacob, D. (2014). The European climate under a 2° C global warming. Environmental Research Letters, 9(3), 034006.

Vozhehova, R. A., Lykhovyd, P. V., Kokovikhin, S. V., Biliaieva, I. M., Markovska, O. Ye., Lavrenko, S. O., & Rudik, O. L. (2019). Artificial neural networks and their implementation in agricultural science and practice. Diamond Trading Tour, Warsaw. 108 pp.

Vozhehova, R. A., Maliarchuk, M. P., Biliaieva, I. M., Lykhovyd, P. V., & Maliarchuk, A. S. (2020). Forecasting the yields of spring row crops by the remote sensing data. Agrarian Innovations, 1, 5-10.

Vozhehova, R., Maliarchuk, M., Biliaieva, I., Lykhovyd, P., Maliarchuk, A., & Tomnytskyi, A. (2020b). Spring row crops productivity prediction using normalized difference vegetation index. Journal of Ecological Engineering, 21(6), 176-182.

Wallington, T. J., Srinivasan, J., Nielsen, O. J., & Highwood, E. J. (2009). Greenhouse gases and global warming. Environ Ecol Chem, 1, 36.

Waltz, E. (2009). GM crops: Battlefield. Nature News, 461(7260), 27-32.

Wang, J., Rich, P. M., & Price, K. P. (2003). Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International Journal of Remote Sensing, 24(11), 2345-2364.

Wang, R., Cherkauer, K., & Bowling, L. (2016). Corn response to climate stress detected with satellite-based NDVI time series. Remote Sensing, 8, 269.

Wang, Y., Yan, X., & Wang, Z. (2015). Global warming caused by afforestation in the Southern Hemisphere. Ecological Indicators, 52, 371-378.

Wheeler, T., & Von Braun, J. (2013). Climate change impacts on global food security. Science, 341(6145), 508-513.

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82.

Willmott, C. J., & Matsuura, K. (2006). On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. International Journal of Geographical Information Science, 20(1), 89-102.

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management science, 6(3), 324-342.

Woldemichael, A., Salami, A., Mukasa, A., Simpasa, A., & Shimeles, A. (2017). Transforming Africa’s agriculture through agro-industrialization. Africa Economic Brief, 8(7), 1-12.

Woodward, F. I., Lomas, M. R., & Betts, R. A. (1998). Vegetation-climate feedbacks in a greenhouse world. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 353(1365), 29-39.

Xiong, F. S., & Day, T. A. (2001). Effect of solar ultraviolet-B radiation during springtime ozone depletion on photosynthesis and biomass production of Antarctic vascular plants. Plant Physiology, 125(2), 738-751.

Xu, C., & Katchova, A. L. (2019). Predicting soybean yield with NDVI using a flexible Fourier transform model. Journal of Agricultural and Applied Economics, 51, 402-416.

Xue, Y. N., Luan, W. X., Wang, H., & Yang, Y. J. (2019). Environmental and economic benefits of carbon emission reduction in animal husbandry via the circular economy: Case study of pig farming in Liaoning, China. Journal of Cleaner Production, 238, 117968.

Yan-e, D. (2011). Design of intelligent agriculture management information system based on IoT. In: Fourth International Conference on Intelligent Computation Technology and Automation. IEEE. (pp. 1045-1049).

Yin, G., Liu, L., & Jiang, X. (2017). The sustainable arable land use pattern under the tradeoff of agricultural production, economic development, and ecological protection – an analysis of Dongting Lake basin, China. Environmental Science and Pollution Research, 24(32), 25329-25345.

Zanetti, S. S., Sousa, E. F., Oliveira, V. P., Almeida, F. T., & Bernardo, S. (2007). Estimating evapotranspiration using artificial neural network and minimum climatological data. Journal of Irrigation and Drainage Engineering, 133(2), 83-89.

Zaval, L., Keenan, E. A., Johnson, E. J., & Weber, E. U. (2014). How warm days increase belief in global warming. Nature Climate Change, 4(2), 143-147.

Zhang, G., Lu, F., Huang, Z. G., Chen, S., & Wang, X. K. (2016). Estimations of application dosage and greenhouse gas emission of chemical pesticides in staple crops in China. The Journal of Applied Ecology, 27(9), 2875-2883.

Zhu, X. G., Long, S. P., & Ort, D. R. (2010). Improving photosynthetic efficiency for greater yield. Annual Review of Plant Biology, 61, 235-261.

Zhukova, M., & Ulez’ko, A. (2019). The specifics of the digital transformation of agriculture. Advances in Intelligent Systems Research, 167, 121-124.

Zinke-Wehlmann, C., De Franceschi, P., Catellani, M., & Dall’Agata, M. (2019). Early within-season yield prediction and disease detection using Sentinel satellite imageries and machine learning technologies in biomass sorghum. In Software Technology: Methods and Tools: 51st International Conference, TOOLS 2019, Innopolis, Russia, October 15–17, 2019, Proceedings (pp. 227-234). Springer Nature.

Zoungrana, B. J. B., Conrad, C., Amekudzi, L. K., Thiel, M., & Da, E. D. (2015). Land use/cover response to rainfall variability: A comparing analysis between NDVI and EVI in the Southwest of Burkina Faso. Climate, 3(1), 63-77.

Авакян, С. В. (2017). Супрамолекулярная физика окружающей среды: климатические и биофизические эффекты. Вестник РАН, 87(5), 456-466.

Адаменко, О. М. (2009). Чим загрожує нам глобальне потепління? Прикарпатський вісник НТШ. Пульс, 4(8), 143-148.

Адаменко, Т. І. (2014). Агрокліматичне зонування території України з врахованням зміни клімату. Київ, ТОВ «РІА» БЛІЦ, 20 с.

Айвазян, С. А. (2001). Прикладная статистика. Основы эконометрики. Том 2. Юнити-Дана, Москва.

Аноним (2020). Новости аграрной науки. Наше Сельское Хозяйство. Агрономия, 21, 100-103.

Баздырев, Г. И., Захаренко, А. В., Лошаков, В. Г., & Рассадин, А. Я. (2019). Земледелие: Учебник. Москва, Инфра-М. 608 с.

Барабаш, В. О. (2019). Глобальне потепління – загроза для Одеси. «Проблеми формування здорового способу життя у молоді». Збірник матеріалів XII Всеукраїнської науково-практичної конференції молодих учених та студентів з міжнародною участю. ФОП Бондаренко М. О., Одеса. (с. 352-253).

Барталёв, С. А., Егоров, В. А., Лупян, Е. А., Плотников, Д. Е., & Уваров, И. А. (2011). Распознавание пахотных земель на основе многолетних спутниковых данных спектрорадиометра MODIS и локально-адаптивной классификации. Компьютерная оптика, 35(1), 103-116.

Басок, Б. І., & Базєєв, Є. Т. (2020). Глобальне потепління: проблеми, дискусії та прогнози. Світогляд, 6(86), 4-15.

Білинський, Й. Й., & Книш, Б. П. (2021). Аналіз характеристик та обґрунтування індексів рослинності. Вісник Вінницького Політехнічного Інституту, (2), 7-14.

Боголепов, М. А. (1907). О колебаниях климата Европейской России в историческую эпоху. Землеведение, Москва. (с. 58-162).

Бондаренко, Л. В., Маслова, О. В., Белкина, А. В., & Сухарева, К. В. (2018). Глобальное изменение климата и его последствия. Вестник Российского экономического университета им. ГВ Плеханова, 2(98), 84-93.

Боума, Э. (2012). Погода и защита растений. BASF: The Chemical Company. 176 с.

Будыко, М. И. (1977). Глобальная экология. Мысль, Москва. 328 с.

Бурляй, А. П. (2021). Точне землеробство як напрям модернізації аграрного виробництва. Modern Economics, 29, 29-34.

Буталенко, Р. Р., Тумаков, І. В., Загрудний, В. В., Рудченко, Ю. І., & Бурлака, О. А. (2021). Інноваційні аспекти операційних технологій у рослинництві. Міжнародна науково-практична конференція «Техніка та технології в агропромисловому виробництві» Полтавський державний аграрний університет (м. Полтава, 07-08 жовтня 2021 р.), 25-27.

Бычков, В. В., Остапов, В. И., Журавлев, А. И., Котляр, Н. М., Лирнык, В. А., Ломоносов, П. И., Писаренко, В. А., & Фунтов, А. П. (1987). Научно обоснованная система земледелия Херсонской области. Херсон, Облполиграфиздат. 448 с.

Вакалюк, Ю. В., & Назаров, И. М. (1991). Проблемы изменения глобального климата. Метеорология и Гидрология, 4, 74.

Вожегова, Р. А., & Лиховид, П. В. (2021). Оцінка точності розрахунків евапотранспірації в мобільному додатку EVAPO. Збірник наукових праць УкрНДІПВТ ім. Погорілого, 29(43), 120-125.

Вожегова, Р. А., Голобородько, С. П., Грановська, Л. М., & Сахно, Г. В. (2013). Зрошення в Україні: реалії сьогодення та перспективи відродження. Зрошуване землеробство, 60, 3-12.

Вожегова, Р. А., Лиховид, П. В., Біляєва, І. М., Лавренко, С. О., & Бойценюк, Х. І. (2020). Модифікований метод Хольдріджа для визначення евапотранспірації. Аграрні Інновації, 3, 17-20.

Войтюк, Д. Г. (2000). Технічні проблеми «точного землеробства» в Україні. Вісник аграрної науки, 9, 41-46.

Войціцький, В. М. (2018). Концепція антропогенноспричиненого глобального потепління: реальність чи науковоподібний міф? Наукові доповіді НУБіП України, 6(76), 9 с.

Воровка, В. П., & Чебанова, Ю. В. (2018). Глобальне потепління: наслідки для М

September 5, 2022

Details about this monograph

ISBN-13 (15)