Soils Under Stress: More Work for Soil Science in Ukraine 3030683931, 9783030683931

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Yuriy Dmytruk David Dent   Editors

Soils Under Stress More Work for Soil Science in Ukraine

Soils Under Stress

Yuriy Dmytruk · David Dent Editors

Soils Under Stress More Work for Soil Science in Ukraine

Editors Yuriy Dmytruk Agrotechnology and Soil Science Department Yuriy Fedkovych Chernivtsi National University Chernivtsi, Ukraine

David Dent Chestnut Tree Farm Forncett End Norfolk, UK

ISBN 978-3-030-68393-1 ISBN 978-3-030-68394-8 (eBook) https://doi.org/10.1007/978-3-030-68394-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Equivalent Terms in the Ukrainian Soil Classification and the World Reference Base

Main mapping units in Polupan M, V Solovei and V Velychko, undated, Soils of Ukraine 1:5 million. Institute for Soil Science and Agrochemistry Research named after ON Sokolovsky, Kharkiv IUSS Working Group WRB 2015 World reference base for soil resources 2014, update 2015. World Soil Resources Rept 106, FAO Rome Alluvial meadow: Mollic Gleyic Fluvisols Alluvial sod-podzolic: Umbric Greyic Fluvisols Alluvial soddy gleyed: Umbric Gleyic Fluvisols Burozems: Cambisols Burozem podzolic: Dystric Cambisols Chernozem humus-skeletal: Rendzic Leptosols/Rendzic Phaeozems Chernozem leached Luvic Chernozem Chernozem ordinary: Haplic Chernozem Chernozem podzolised: Greyzemic Luvic Phaeozems Chernozem podzolised top-gleyed: Stagnic Greyzemic Phaeozems Chernozem southern: Calcic Chernozem Chernozem typical: Vermic Haplic Chernozem Cinnamon soils: Luvic Kastanozem Dark kastanozem: Luvic Kastanozem Dark grey forest soils: Greyzemic Luvic Phaeozem Dark grey podzolised soils: Albic Luvisols, Humic Grey forest gley soils: Gleyic Luvic Phaeozem Grey forest soils: Albic Luvisols Light grey forest soils: Albic Luvisols Light grey forest, top-gleyed: Stragnic Albic Luvisols Meadow chernozem: Gleyic Chernozem Meadow kastanozem: Gleyic Kastanozem Peat: Histosols Solonchak: Solonchak Solonetz: Solonetz

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About This Book

Vasily Dokuchaev carried out most of his research in Ukraine. His student and friend, Volodymyr Vernadsky, went on to create trans-disciplinary environmental sciences and the concept of Earth as a living organism, famously taken up by James Lovelock. That spring of ideas still flows and the researches captured in this volume relate to a host of present-day problems, and not only in Ukraine. Soils have always been under stress but, in the Anthropocene, mankind is in the driving seat. As a sequel to Soil Science Working for a Living: Applications of Soil Science to Present-Day Problems, we consider issues of policy as well as soil genesis, attributes and functions in various environments, natural and man-made. We consider human impacts on the soil cover through its use and misuse, highlight methods of research and assessment of soil quality, and the threats of soil degradation. The distinguished contributors also describe and propose various options for evaluation and remediation of degraded soils, drawing on the latest methods of modelling and cartography as well as field experiments and long experience. The book will be invaluable to researchers and practitioners in soil science including graduate and post-graduate education, academics and professionals.

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Seventy years ago, Chernivtsi National University, in Ukraine, began teaching soil science as a separate discipline. This volume celebrates this anniversary and soil science across the country. We have much to celebrate. Vasily Dokuchaev conducted most of his research here, in Ukraine. The first President of the Academy of Sciences was his student and friend, Volodymyr Vernadsky who went on to create transdisciplinary environmental sciences and the concept of Earth as a living organism, famously taken up by James Lovelock (1979). Other founders of the Ukrainian school of soil science include Oleksandr Nabokykh, Volodymyr Krokos, Hryhorii Makhov and Oleksii Sokolovskyi whose name is taken by the leading research centre of soil science, agrochemistry and soil conservation in Kharkiv. Soil science faced pressing problems in the 1950s. As many as 2.8 million people had starved to death in the Ukrainian countryside in the 1946-7; in some places, famine lasted till the end of the 1940s. There was need to break new ground in agriculture for better management of soil fertility, and for a large-scale soil map of the country. Large-scale soil surveys fostered regional schools of soil science; Petro Kuchynsky started such a school at Chernivtsi University and its main fields of research are still soil formation, soil conservation and better soil management. These issues are still with us, and not only in Ukraine. This is not because we have failed to make progress: we have made great strides. The COVID-19 crisis is not a food crisis; the food system has proven remarkably resilient, it has kept people fed, affordably, and kept farmers and farm labour in place. But business-as-usual is unsustainable—indeed, uninvestable. That is to say, it is uninvestable for private investors because farm-gate prices do not meet the cost of production. The real cost of food includes the relentless degradation of soil and water resources, mass extinction of species and climate change—none of these is accounted in the price of food. The outside world should be grateful that countries like Ukraine are giving away their environment along with their food exports! Climate change, already with us, is inextricably bound up with soil use and management. Soils hold five times as much carbon the as the atmosphere—more than the atmosphere and all standing vegetation together—but farmers have been burning off soil carbon for twelve thousand years. They are responsible for one third of greenhouse gas emissions. They have run up an enormous carbon debt; the better xi

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the soil, the bigger the debt. So, Part I deals with Policy Issues. Anatolii Kucher and his colleagues present a holistic concept of sustainable management of soil organic carbon and a framework for implementation that can deliver a stable level of soil carbon now and, from now on, gradual regeneration. David Dent and his colleagues follow up with An investable proposal for specific actions across the steppes. Greenhouse gas emissions can be reversed by crop rotations that will produce not only greater cash crops but, also, enough biomass to generate biogas and electricity to replace all our coal-fired power stations; those power stations have to be replaced in any event; and the finance is there for the asking. The status of soil information is another pressing issue. Ukraine’s fundamental information is incomplete and unreliable; a quarter of the country has never been surveyed; there is no information for the hills and forests, or for built up areas where the need for information is the greatest. The information we do have is, at best, 30 years old and the underlying systematic survey is 60 years old: the soils have changed and so has the information that we need. In Status and problems of normative monetary valuation of land, Igor Iatsuk and his colleagues highlight systematic errors that were introduced in the process of geodetic rectification and digitization of the original paper maps. It is troubling that, for legal reasons, the only safe course is to excise the unreliable data and use regional average land prices instead. It should also be troubling that, in the absence of political will, a fully functional modern geoinformation environment has been held back and a whole generation of technological solutions developed in various fields are not implemented. Those solutions include predictive soil mapping using digital terrain models. For instance, using an app that draws down already-available gridded soil data (download at LandPotential.org) citizen soil survey can now do a better job than the heroic first generation of soil surveyors. Our predictive models are no longer held only in the heads of the surveyors, they can be digitally recorded so surveys can be repeated, up-scaled and extended as required and as resources allow (Herrick and others 2016, Quandt and others 2020). Part II, Pedology and soil survey, spans some technological solutions to the dearth of reliable information, as well as research to better understand soil processes and patterns, and their practical implications. Digital soil mapping—and much else besides—depends on detailed and reliable digital elevation models (DEMs). These are now within easy reach thanks to open-source software and advances in unmanned aerial vehicles (UAVs). In Creating digital elevation models using budget UAVs, Andriy Achasov and his colleagues compare DEMs constructed from data acquired by six different middle-price UAVs carrying eight different cameras at three flying heights. DEM construction was carried out with reference to ground control points and without such reference using the coordinates of the onboard global positioning systems. The use of ground control points did not always improve the quality of the model but, even taking account of their discrepancies, all resulting DEMs were good enough to make large-scale, topographic and thematic maps. No aspect of the changing soil cover is more dramatic than wholesale erosion of the topsoil. The sub-types of Chernozem that are distinguished in Ukraine differ not so much in their diagnostic horizons as in their thickness—so the soil pattern is much changed by erosion. Sergiy Chornyy, Dmitriy Abramov and Daria Sadova

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demonstrate a powerful tool to monitor this situation in Determination of eroded Chernozem on the Right-Bank Steppe of Ukraine using the Soil Line. The soil line is the linear relationship between red and near infra-red light reflected by bare soil and measured by multispectral scanners aboard Earth-orbiting satellites. The parameters of the soil line and the magnitude of the coefficient of determination reflect the content of humus, carbonates and salinity, so they can be used to identify and map various sub-types of Chernozem and their eroded variants. Identification of the structure of soil cover by magnetic susceptibility by Mykola Miroshnychenko, Olexander Kruglov and their colleagues explores the application of a technique originally developed for minerals exploration to map agronomically important soil attributes. Based on magnetic susceptibility, spatial heterogeneity was generalized and described for elementary geochemical landscapes, each with a particular CNPK composition and pH. This might be a convenient way to implement the principles of precision farming. Specificity of processes in hydromorphic soils by Roman Truskavetskiy and his colleagues illustrates the development of gley morphology by experiment, distinguishes soil attributes associated with surface waterlogging and those associated with shallow groundwater, and considers the implications for management. Shallow groundwater promotes accumulation of soil organic matter, nitrogen and phosphates and, ultimately, peat formation. Seasonal surface waterlogging decreases total and mobile phosphates but a wide range of crops can be grown on these soils without costly amelioration. The most common pattern of dependence of the phosphate regime on the soil-forming process is with soil organic matter. This theme is developed by Tetiana Tsvyk in Anthropogenic and genetic conditions for phosphate mobility in individual structural fractions of Podzolized chernozem. She observes further relationships with land use and with the silt fraction embedded in structural aggregates; the degree of phosphate mobility depending on soil structure is one of the main indicators of the availability of phosphorus to plants. The different content of mobile phosphorus in separate structural size fractions and its dependence on the embedded silt fractions is remarkable. Oksana Tsurkan and Svetlana Burykina reveal further subtleties in the behaviour of soil organic matter in Changes in quantity and quality of soil organic matter in Calcic chernozem under different systems of fertilization. During 45 years under cropping without fertilization, the total carbon content of the plough layer decreased from 1.56 per cent to 1.46 per cent. Under combined organic and mineral fertilization, total carbon increased relative to the unfertilized control and, even, absolutely under a base application of 45t farmyard manure per ha of the crop rotation and higher doses of mineral fertilizers. However, both low doses of mineral fertilizers (N300 P150 K150 ) and higher-than-average applications (N900 P300 K300 ) along with farmyard manure contributed to a transition in the kind of humus from a humate to fulvate-humate type, as did increased doses of mineral fertilizers alone. Steppe soils are assailed by various agents of degradation. Afforestation is an effective way to arrest soil degradation but its effects on soil physical attributes are poorly understood. In Robinia pseudoacacia and Quercus robur plantations change the physical properties of Calcic chernozem, Vadym Gorban shows that, compared with arable land, the soil under woodland exhibits a lesser clay content,

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more silt and sand, and a shift towards larger water-stable aggregates and greater total porosity. Yuriy Dehtyiar’ov and his colleagues investigate the formation, structure and resilience of soil aggregates, in particular micro-aggregates in Typical chernozem. Humus-accumulative soil formation is active under woodland as well as under perennial grassland, so restorative management of Chernozem renews all the physical indicators of soil fertility whereas the same indicators reveal the opposite direction of travel under arable. Coming from the opposite pedological pole, Volodymyr Yakovenko and Olexandr Zhukov’s Zoogenic structure aggregation in steppe and forest soils describes ecosystem engineering by soil macrofauna. In Calcic chernozem under herbaceous vegetation and oak plantations, the droppings of insect larvae are an important component of the topsoil, but the most intense zoogenic activity is observed under native forest where topsoils are made up almost entirely by earthworm casts and, deeper in the profile, the burrowing activity of invertebrates and transport of organic matter sets the stage for deep ramification of roots, infiltration of rainfall and transmission of water to streams and groundwater. In Part III, Regional assessments include drained and technogenically degraded peatlands in Western Polissia by Andrii Bortnik, Volodymyr Gavryliuk and Tetyana Bortnik; and the farmland of the Hologoro-Kremenetskiy Highlands by Oksana Haskevych and her colleagues from Lviv. Both chapters advance novel approaches to complexity. Comparative evaluation of the sandy soils of pine forests in Ukraine, by Svitlana Raspopina, Vasylii Degtyarjov and Olena Chekar, is spectacularly illustrated and demonstrates the similarity of productive potential of Umbric albic podzols and Leptic podzols. Although these soils appear very different, they have an important attribute in common—a very coarse texture that determines the similarity of their forestry potential. Other things being equal, a soil with less than 5 per cent clay will produce pine stands of bonitat class II; soils with 5–7 per cent clay will yield bonitat class I-Ia; soils with 7–12 per cent clay will yield class Ia-b. Where there is a pronounced seasonal water deficiency (Northern Steppe, Forest-Steppe) the determining factor is the ability of clay particles to retain water: where there is enough water (Polissia), nutrients are likely to be the key. Part IV, Better management draws on a wealth of experience. Across the steppes, soil fertility has declined through neglect of crop rotation, a big reduction in the area under legumes and perennial grasses, and lack of farmyard manure. Valentyna Gamajunova and her colleagues in Mikolayiv and Kherson underscore the direct and residual effects of farmyard manure and crop residues, applied separately and with mineral fertilizers, on humus content, water holding capacity and microbiological activity. Organic fertilizers and incorporation of straw and stubble increase the soil’s capacity to absorb rainfall and significantly reduce unproductive evaporation, but the main source of organic matter for arable soils is now crop residues. Processing fresh organic materials with a commercial stubble bio-decomposer accelerates decomposition with less dependence on the weather. In view of the growing popularity of liquid nitrogen fertilizer, Mykola Miroshnychenko and colleagues at Kharkiv report on The effect of anhydrous ammonia on Chernozem quality and crop yields. Anhydrous ammonia was applied at a dose of 100kgN/ha on fine loamy Luvic chernozem in Poltava Province. Anhydrous ammonia

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offers a significant cost and yield advantage over ammonium nitrate. During three years, this resulted in no changes in physical, chemical and biological properties that would indicate soil degradation but trends of acidification, increasing humus mobility, and changes in the structure of the microbial community were observed, mostly in the first few days after application. These changes should be monitored if anhydrous ammonia is used continually. Yevhen Skrylnyk, Viktoriia Hetmanenko and their colleagues investigate the Influence of reduced tillage and organo-mineral fertilization on soil organic carbon and available nutrients in Typical Chernozem. Under a 4-field arable rotation, various combinations of organo-mineral fertilization and tillage intensity increased the SOC stock compared with the unfertilized control. Application of manure and manure + NPK was more effective than return of crop residues, especially in case of reduced tillage. Accumulation of humin under reduced tillage indicates increasing resistance of soil organic matter to decomposition, whereas under conventional tillage, an accumulation of the humic acid fraction indicates greater humification; unsurprisingly, the more tillage the greater the content of available nutrients. But if the aim is to increase soil organic matter stocks, where are the long-term field experiments on zero tillage? Degradation of weakly acidic soils can be counteracted cheaply by phytoameliorant crops. This helps solve three big problems of modern agriculture: the maintenance of soil fertility, improving the agricultural-ecological status of the soils, and providing forage for livestock. Yurii Tsapko and his colleagues have demonstrated that growing perennial grasses, lucerne and sainfoin for three years without any fertilizers or other chemical inputs is an effective, resource-saving and environmentally friendly way of neutralizing soil acidity in Podzolized chernozem. A crop rotation of lupin → mustard → soybean also raised soil pH from 5.7 to 5.9 in the control to 6.0–6.1. And giant miscanthus, apart from producing a lot of biofuel, improved the agricultural-ecological state of Podzolized chernozem, reduced soil acidity and activated mesofauna and the accumulation of soil organic matter and plant nutrients. Falling soil fertility has made optimization of plant nutrition all the more important. In Advances in nutrition of sunflower on the Southern Steppe of Ukraine, Oleg Kovalenko and colleagues test various combinations of micronutrients and biopreparations for processing seed material and foliar fertilization of hybrid sunflower. Compared with the control, optimal nutrition from sowing to harvest increased the growing season by 2–13 days, increased the height of plants during the flowering period by 4–13 per cent, and raised the yield of seed by almost 30 per cent and the yield of crude oil by 0.424t/ha. Climate change has already brought higher summer temperatures and more frequent droughts. Refurbishment of irrigation schemes is now a priority and drip irrigation is one of the most efficient technologies; it can apply irrigation water and nutrients to the root zone uniformly and precisely and, up to a point, it is environmentally friendly. Salt regime of soils under drip irrigation by Sviatoslav Baliuk, Maryna Zakharova and their colleagues generalizes the findings of long-term field experiments across the country. They find that drip irrigation with water of first-class quality causes no significant changes in the water cycle. Second-class water causes

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temporary, local salinization that can easily be remedied. But third-class, brackish water is unsuitable for irrigation; it causes salinity and sodicity. Part V confronts Restoration of technogenically degraded soils. Long-term investigations of contaminated areas in Ukraine have fostered a suite of remedial methods and techniques. Reporting on Innovative methods to remediate polluted soils, Valentina Samokhvalova deals with prediction of the ecological state of the soil in polluted zones and technology to detoxify heavy metals in the soil-plant system by removal or by rendering them harmless. This is a foundation for further fundamental research on soil quality, ecological management to arrest soil degradation and regenerate the use and function of polluted soils, new algorithms and problemsolving tools, new opportunities for effective remedies—and proven environmental and economic benefits of their application. Soil transformation on restored drill pads of oil-gas fields in Eastern Ukraine by Olena Drozd, Dmytro Diadin, Oksana Naidonova and Tetyana Klochko covers the identification of disturbed sites by remote sensing and the assessment of the condition of restored soils. Satellite imagery reveals significant contrast in spectral brightness of the restored soils compared with undisturbed Chernozem, especially at drilling sites restored more than 40 years ago where site restoration resulted in soil compaction and contamination by barytes and heavy metals. The thickness of the humus-rich layer of restored soils is less than in natural Chernozem and it is mixed, to some degree, with subsoil. Microbiological indicators are sensitive to soil pollution by heavy metals, components of drilling mud, wastewater, etc. They are particularly informative of the state of contamination, revealing the hidden biological consequences, rate of rehabilitation and appropriate measures for soil improvement.

References Herrick, J.E., A. Beh, E. Barrios, D.L. Dent, and others. 2016. The Land Potential Knowledge System (LandPKS): Mobile apps and collaboration for optimising climate change investments. Ecosystem Health & Sustainability 2: 3 oe01209.doc10.1402/eksz.1209). Lovelock, J.E. 1979. Gaia. A new look at life on Earth. Oxford: Oxford University Press. Quandt, A., J.E. Herrick, G. Peacock, and others. 2020. A standardized land capability classification system for land evaluation using mobile phone technology. Journal of Soil and Water Conservation. https://doi.org/10.2489/jswc.2020.00023.

Contents

Part I 1

2

3

Conceptualizing Sustainable Management of Soil Organic Carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anatolii Kucher, Lesia Kucher, and Antonina Broyaka

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Status and Problems of Normative Monetary Valuation of Land in Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Igor Iatsuk, Yuriy Dmytruk, Vasyl Cherlinka, and David Dent

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An Investable Proposal to Transform the Steppe . . . . . . . . . . . . . . . . . David Dent, Boris Boincean, and Zhanguo Bai

Part II 4

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Policy Issues

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Pedology and Soil Survey

Creating Digital Elevation Models Using Budget Unmanned Aerial Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrii Achasov, Arkadiy Siedov, Alla Achasova, Ganna Titenko, and Oleg Seliverstov Determination of Eroded Chernozem on the Right-Bank Steppe of Ukraine Using the Soil Line . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergiy Chornyy, Dmitriy Abramov, and Daria Sadova Identification of the Structure of Soil Cover by Magnetic Susceptibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mykola Miroshnychenko, Oleksandr Kruglov, Pavlo Nazarok, and Stanislav Kovalenko Specificity of Processes in Hydromorphic Soils . . . . . . . . . . . . . . . . . . . Roman Truskavetskiy, Victoriya Zubkovskaya, Iryna Khyzhniak, and Natalya Palamar

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Anthropogenic and Genetic Conditions for Phosphate Mobility in Individual Structural Fractions of Podzolized Chernozem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tetiana Tsvyk

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Changes in Quantity and Quality of Soil Organic Matter in Calcic Chernozem Under Different Systems of Fertilization . . . . . Oksana Tsurkan and Svetlana Burykina

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10 Robinia Pseudoacacia and Quercus Robur Plantations Change the Physical Properties of Calcic Chernozem . . . . . . . . . . . . . . . . . . . . . Vadym Gorban

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11 Transformation of Physical Indicators of Soil Fertility in Typical Chernozem of the Eastern Forest-Steppe of Ukraine . . . . . 105 Yurii Dehtiar’ov, Dmytro Havva, Natalia Kovalzhy, and Sergiy Rieznik 12 Zoogenic Structure Aggregation in Steppe and Forest Soils . . . . . . . . 111 Volodymyr Yakovenko and Olexandr Zhukov Part III Regional Assessments 13 Status and Future of Drained and Technogenically Degraded Peatlands in Western Polissia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Andrii Bortnik, Volodymyr Gavryliuk, and Tetyana Bortnik 14 Agro-Ecological Assessment of the Farmlands of the Hologoro-Kremenetskiy Highlands . . . . . . . . . . . . . . . . . . . . . . . . 143 Oksana Haskevych, Volodymyr Snitynskyy, Petro Hnativ, Natalia Lahush, Volodymyr Haskevych, and Viktor Ivaniuk 15 Comparative Evaluation of the Sandy Soils of Pine Forests in Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Svitlana Raspopina, Vasylii Degtyarjov, and Olena Chekar Part IV Better Management 16 Better Management of Soil Fertility in the Southern Steppe Zone of Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Valentyna Gamajunova, Antonina Panfilova, Oleh Kovalenko, Lyubov Khonenko, Tetyana Baklanova, and Olena Sydiakina 17 The Effect of Anhydrous Ammonia on Chernozem Quality and Crop Yields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Mykola Miroshnychenko, Alina Revtie-Uvarova, Yevheniia Hladkikh, and Yuliia Krupoderia

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18 Influence of Reduced Tillage and Organo-Mineral Fertilization on Soil Organic Carbon and Available Nutrients in Typical Chernozem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Yevhen Skrylnyk, Viktoriia Hetmanenko, Anzhela Kutova, Kateryna Artemieva, and Yurii Tovstyi 19 Phyto-Amelioration of Podzolised Chernozem . . . . . . . . . . . . . . . . . . . . 197 Yurii Tsapko, Yana Vodiak, Alyona Kholodna, Viacheslav Kalinichenko, and Albina Ogorodnya 20 Salt Regime of Soils Under Drip Irrigation . . . . . . . . . . . . . . . . . . . . . . 205 Sviatoslav Baliuk, Maryna Zakharova, Ludmila Vorotyntseva, Alexander Nosonenko, and Yuri Afanasyev 21 Advances in Nutrition of Sunflower on the Southern Steppe of Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Oleg Kovalenko, Valentyna Gamajunova, Ruslan Neroda, Irina Smirnova, and Lyubov Khonenko Part V

Restoration of Technogenically Degraded Soils

22 Innovative Methods to Remediate Polluted Soils . . . . . . . . . . . . . . . . . . 227 Valentina Samokhvalova 23 Soil Transformation on Restored Drill Pads of Oil-Gas Fields in Eastern Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Olena Drozd, Dmytro Diadin, Oksana Naidonova, and Tetyana Klochko

Part I

Policy Issues

He that will not apply new remedies must expect new evils. Of Innovations, Francis Bacon, 1625

Chapter 1

Conceptualizing Sustainable Management of Soil Organic Carbon Anatolii Kucher , Lesia Kucher , and Antonina Broyaka

Abstract Over the last 50 years, the loss of soil organic carbon has cost Ukraine an average of $US985.6 million (25.2 billion UAH) a year in terms of the capital value of the land. Long-term trends and the current state of the weighted average carbon content in arable soils indicate a worsening situation. Sustainable management of soil organic carbon (SOC) could be a foundation for handling or, even, solving several critical issues including land degradation, sustainable agriculture and climate change. In this context, our holistic concept of sustainable SOC management considers the goal block, the subject-object block, the information block, the organizational-andtechnological block and the result block. For practical implementation, we have developed a conceptual framework for sustainable management of SOC that should deliver a stable level of SOC (not lower than 2010) by 2020 and a gradual increase, by no less than 0.1%, by 2030. Keywords Soil organic carbon · Sustainable management · Climate change · Project approach · Holistic concept

A. Kucher (B) Department of Ecology and Neoecology, VN Karazin Kharkiv National University, 6 Svobody Square, Kharkiv 61022, Ukraine e-mail: [email protected] Department of Scientific-Economic Activities, Innovation and Coordination of International Cooperation, NSC Institute for Soil Science and Agrochemistry Research (ON Sokolovsky), 4 Chaikovska St, Kharkiv 61024, Ukraine L. Kucher Department of Applied Economics and International Economic Relations, VV Dokuchaev Kharkiv National Agrarian University, Kharkiv 62483, Ukraine e-mail: [email protected] A. Broyaka Department of Economics, Vinnytsia National Agrarian University, Soniachna St. 3, Vinnytsia 21000, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Dmytruk and D. Dent (eds.), Soils Under Stress, https://doi.org/10.1007/978-3-030-68394-8_1

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Introduction Sustainable soil management and innovative soil management practices are topical issues worldwide (Adeyolanu and Ogunkunle 2016; Ansong Omari and others 2018; Baritz and others 2018; Er¸sahin and others 2017; Helming and others 2018; Osman 2018; Rojas and Caon 2016; Vargas and others 2016). In Ukraine, they are still at an early stage, and our 2019 monograph was a first attempt to close the research gap (Kucher 2019). Sustainable soil management should be a priority in Ukraine because the country boasts the best soil in the world: with 282 million ha of the Chernozem, Ukraine ranks second to Russia (1061 million ha) in her area of Chernozem and, in respect of all Black Earths (Chernozem, Kastanozem and Phaeozem), fourth in the world after Russia, the USA and China. However, under the onslaught of industrial farming, Chernozem everywhere has undergone profound but largely unnoticed changes with far-reaching consequences. SOC plays a pivotal role in soil health, fertility and ecosystem services FAO (2017a). By 2050, farmers must grow 60–70% more food than now—and the best soils have the greatest potential. Food production accounts for 92% of society’s water consumption, and nearly all freshwater is delivered by the soil (Allan and others 2019). Carbon stocks in the topmost metre of soil are estimated at 1550 Gt, and the inorganic carbon stock in the topmost metre of soil is 750 Gt (Lal 2004). The atmospheric stock is about 820 Gt at present but it is increasing by as much as 5 Gt per year so the ratio of carbon stocks in the top metre of soil and the atmosphere is about 2.8 to 1. If we consider the whole global soil profile and also add permafrost carbon, the ratio is more than 5. IPCC (2019) concluded that carbon emissions from land-use change account for up to 20% of atmospheric CO2 but Boincean and Dent (2019, 2020) reckon that the real figure is closer to 33% because mineralization of soil organic matter was not fully accounted. At the same time, carbon-depleted soils have a huge potential for carbon capture. They can store up to 760–1520 Gt of additional carbon atmosphere (FAO 2017b), and since the 2016 Paris summit, attention has been paid to the soil’s potential for carbon capture through the French 4 per mille initiative. Zdruli and others (2017) also argue that the management of soil organic carbon can increase plant productivity by increasing water use efficiency, optimizing nutrient cycles and increasing vegetation cover, thereby increasing food security. Even small changes in total SOC have big effects, particularly on soil physical properties (Powlson and others 2011). All this demands consideration of the role of Ukrainian soils and the management of soil organic carbon as a main factor for global food security and resilience of agriculture in the face of climate change. In the immediate future, climate change will likely transform some part of Ukrainian agriculture through more and more severe droughts (Shvidenko and others 2017). These issues are being investigated by scientists of the NSC Institute for Soil Science and Agrochemistry Research named after ON Sokolovsky (NSC ISSAR), including Baliuk and others (2017), Baliuk and Kucher (2019), Kucher (2015a, 2016a) and colleagues. Here, we highlight some

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conceptual issues that need to be resolved in order to strengthen the resilience of Ukrainian agriculture.

Materials and Methods The holistic paradigm is fundamental to understanding the integrity of processes and phenomena. In this study, we have applied theoretical generalization and formulation of conclusions; analysis and synthesis of project indicators; in-depth economic and statistical analysis of the issues and relevant data; maps to display the results across the regions and the country; calculation and expert judgement of the parameters of the conception and its efficacy. The database of the average content of humus in arable soils in the regions and soil and climatic zones of Ukraine was used as the empirical basis.

Results and Discussion Loss of SOC in Arable Soils Since Dokuchaev’s day, there have been many measurements of soil organic matter across the country. They show an average loss of humus over these 140 years of 22% across the forest-steppe, 19.5% in the steppe and about 19% in the Polissya zone. The annual humus loss amounts to 550–600 kg/ha,—sometimes considerably more (Baliuk and Medvedev 2012), although many records of bigger losses may be ascribed to uncertainty as to the exact location of the initial measurements, as well as the wide and very local variability of soil humus content, even in virgin land (Medvedev 2009). Table 1.1 summarizes the current state and trends in Ukraine: The humus content of arable soils has declined almost everywhere. Over the past 10 years, it has decreased most significantly in Donetsk, Zaporizhzhya and Khmelnytskyi regions (−0.37, −0.12 and −0.12%, respectively). At the same time, the picture has improved in some places: the most significant increases were recorded in Odesa (+0.42%) and in the Polyssia zone in Chernivtsi (+0.30%), Lviv (+0.19%) and Ivano-Frankivsk (+0.18%) where agriculture has retreated from unproductive land. In Ukraine as a whole, the humus content of arable soils decreased in the period 1961–2010 but recent years have seen some recovery although humus levels are still far from their optimal level. Measurements in long-term field experiments demonstrate that prolonged cultivation without adequate manure and fertilizer brings about significant loss of humus throughout the soil profile (Table 1.2). The greatest losses occurred under intensive row crops in the period 1960–80. Subsequently, annual application of organic and mineral fertilizers reached 8.4 t/ha and 170 kg/ha of active ingredients which maintained an equilibrium of humus and

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Table 1.1 Current state and trends of the weighted average content of humus in the soils of arable land in regions and zones of Ukraine Regions

10 stage + – to

Stage (round) of survey 1961 (1 stage)

1991–1995 (6 stage)

2006–2010 (9 stage)

2011–2015 (10 stage)

1 stage

9 stage

Crimea

2.8

2.50

2.48

No data

-

Vinnytsya

3.1

2.78

2.70

2.70

−0.40

0.00

Volyn

1.8

1.84

1.59

1.56

−0.24

−0.03

Dnipro

4.3

3.80

3.83

3.77

−0.53

−0.06

Donetsk

4.6

4.37

4.17

3.80

−0.80

−0.37

Zhytomyr

2.3

2.01

1.92

2.01

−0.29

0.09

Zakarpattya

3.1

2.21

2.48

2.56

−0.54

Zaporizhzhya

3.4

3.32

3.52

3.40

0.00

−0.12

Ivano-Frankivsk

2.8

3.12

3.10

3.28

0.48

0.18

-

0.08

Kyiv

3.3

2.60

2.90

2.98

−0.32

0.08

Kirovohrad

4.8

4.20

4.10

4.11

−0.69

0.01

Luhansk

4.7

4.18

3.95

3.91

−0.79

−0.04

Lviv

2.5

2.26

2.48

2.67

0.17

0.19

Mykolayiv

4.1

3.41

3.15

3.24

−0.86

0.09

Odesa

3.7

3.57

3.35

3.77

0.07

0.42

Poltava

4.3

3.57

3.26

3.18

−1.12

Rivne

2.3

2.23

2.15

2.27

−0.03

0.12

Sumy

4.0

3.37

3.58

3.50

−0.50

−0.08

Ternopil

3.3

3.16

3.14

3.13

−0.17

−0.01

Kharkiv

5.3

4.40

4.20

4.10

−1.20

−0.10

Kherson

2.6

2.32

2.39

2.45

−0.15

0.06

Khmelnytskyi

3.1

3.27

3.08

2.96

−0.14

−0.12

Cherkasy

3.5

3.25

3.12

3.06

−0.44

−0.06

Chernivtsi

3.4

2.40

2.30

2.60

−0.80

0.30

Chernihiv

2.2

2.27

2.47

2.41

0.21

Ukraine

3.5

3.28

3.14

3.16

−0.34

0.02

Steppe

3.8

3.60

3.40

3.45

−0.35

0.05

−0.08

−0.06

Forest-steppe

3.8

3.32

3.19

3.21

−0.59

0.02

Polissya (Forest)

2.2

2.25

2.24

2.33

0.13

0.09

Source data Soils Protection Institute of Ukraine

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Table 1.2 Humus content (%) in virgin (non-turf) and ploughed Chernozem after Baliuk and others (2017) Depth, cm

Chernozem Typical chernozem, virgin land

Typical chernozem, arable

Ordinary chernozem, uncultivated arable

Ordinary chernozem, arable

0–10

7.76

4.58

4.61

4.25

10–20

6.08

4.55

4.35

4.20

20–30

5.05

4.51

4.28

4.12

30–40

4.79

4.29

3.74

3.48

40–50

4.05

3.85

2.80

2.61

50–60

3.82

3.60

2.65

2.49

nutrients, but fertilization fell away again and the humus balance became negative. Recently, the application of mineral fertilizers has increased but farmyard manure is almost unobtainable. NSC ISSAR research (Baliuk and Medvedev 2012) shows annual humus losses of as much as 620 kg/ha (SOC loss 36 kg/ha) caused by extensive ploughing (56% of the land area), insufficient farmyard manure (in the last 10 years less than 1 t/ha has been applied instead of the recommended 8–14 t/ha), unbalanced use of mineral fertilizers, and failure to respect crop rotation with the virtual elimination of perennial grasses and legumes. It might be worse: Boincean and Dent (2019) report a range of SOC loss of 2.4–3.8 tC/ha/yr from ploughland in Moldova, depending on the crop rotation—or lack of it.

Economic Assessment of Stocks of SOC and Loss of Carbon Figure 1.1 maps the worth of organic carbon in the arable layer of farmland in the regions of Ukraine for different rounds of the agrochemical survey. In generalizing the assessed worth of SOC stocks, we grouped the soils according to their humus content: (i) low-humus soils (up to 3%) are classified as low worth (up to 15 thousand $US/ha); (ii) soils with an average humus content (3–4%) have an average worth of 15–20 thousand $US/ha); (iii) soils with a humus content of more than 4% are classed as high worth (over 20 thousand $US/ha). A negative trend is immediately apparent: in the first round of the survey, eight regions were rated low; in the tenth round, there were nine (excluding the Autonomous Republic of Crimea). At the same time, the number of regions with a high economic assessment decreased from eight in the first round to three in the tenth. Taking into account the area of the surveyed lands in the 10th round, the total average annual damage from the loss of soil organic carbon in Ukraine over the past 50 years amounted to $US985.6 million (25.2 bln UAH), including $US493.4 million (12.6 bln UAH) over the last 25 years. The only

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Fig. 1.1 Economic assessment of soil organic carbon in the 0–30 cm layer of arable soils in Ukraine (a) 1961, (b) 2011–15 based on data of Soils Protection Institute of Ukraine

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conditionally good news is the relative decrease in the average annual losses over the past 25 years compared with their value over the 50-year period. This situation and our previous studies (Kucher 2015a, 2019) led us to a holistic concept of sustainable management of SOC in the context of climate change (Fig. 1.2). It consists of a series of blocs, in the sense of a group combined, each or all of which may also be a block, in the sense of a barrier to the achievement of sustainability: a goal block, a subject-object block, an information block, an organizational-andtechnological block and a result block. 1. Mission and strategic goals of sustainable management of SOC in the context of climate change Forecast calculations suggest two possible scenarios: either further loss of soil organic carbon under business-as-usual or stabilization of SOC content given decisive action to maintain equilibrium. In 2018, the Coordination Council for Combating Land Degradation and Desertification approved proposals submitted by the National Academy of Agrarian Sciences of Ukraine on Maintaining the content of organic matter (humus) in soils (MENRU 2018). Given this commitment, the mission is: Stabilization of the organic carbon content of agricultural soils to adapt to climate change, to strengthen the resilience of Ukrainian agriculture and to increase its competitiveness.

The strategic objectives are: i. Stabilize the humus content in arable soils (not lower than the baseline) by 2020. ii. Increase the humus content of arable soils by no less than 0.1% by 2030 including by 0.10–0.16% in Polissya, and by 0.08–0.10% in the Forest-steppe and Steppe. As a baseline, the humus content of arable soils as of 2010 was chosen: 3.14% countrywide, embracing 2.24% in Polissya, 3.19% in Forest-steppe, and 3.4% in Steppe. 2. Information support for sustainable management of SOC in the context of climate change The basic information needed to support decisive action is: 1. The content (per cent) and stocks of humus (t/ha) in the soils of agricultural land according to agrochemical survey; 2. The content and stocks of soil organic carbon in the 0–30 cm layer according to the National and Global Soil Organic Carbon Map; 3. Remotely sensed data on the productivity of agricultural land; 4. Other sources of information on humus and soil organic carbon content. For more than 50 years, an agrochemical survey of farmland has been carried out every five years whereby, for each field, 20 soil indicators are measured including average humus content determined by Tyurin’s method (Aleksandrova and Naidenova 1976). In the ninth round (2006–2010), 26 million ha of farmland was surveyed, 2.7 million topsoil samples were analysed, and more than 450 thousand agrochemical passports were issued for individual fields (Iatsuk 2015). Field passports are

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Note

direct connection (relationship)

feedback

Fig. 1.2 Logical-semantic model of the holistic conception of sustainable management of SOC

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legal documents prepared for land users and landowners with recommendations on how to use and improve the soil. In the 10th round (2011–2015), 19.8 million ha was surveyed, 1.9 million topsoil samples were analysed, and more than 360 thousand agrochemical passports were issued (Iatsuk 2018). 3. Measures of achievement 3.1. Improving of institutional, informational and organizational support i.

Adoption and implementation of normative-legal acts on economic stimulation for soil conservation and maintaining SOC, and measures against landowner/users responsible for loss of SOC; ii. Standards and regulations on soil organic matter management and the production and use of organic fertilizers; iii. Establishment of an all-Ukraine system of soil monitoring, including SOC indicators. 3.2. Technological support of sustainable management of SOC (MENRU 2018) 3.2.1. Increase the inputs of organic matter to the soil i. Increase of crop yields; ii. Changes in the structure of cultivated areas with an increase in the proportion of legumes and inclusion of green manure crops in crop rotations; iii. Stimulation of livestock development, including creation of common hayfields and pastures; iv. Greater production and use of organic fertilizers, including from secondary organic raw materials (processing waste into fertilizers) and local natural resources (sapropel, peat, compost); v. Stimulating organic farming. 3.2.2. The prevention/minimization of losses of soil organic matter Rationalizing arable land by retreat from slopes steeper than 7o and other unsuitable land; restoration of degraded lands; ii. Maintaining shelterbelts, establishing new ones and other protective plantations, including transfer of ownership to the effective managers; iii. Adoption of soil protection technologies including zero tillage; iv. Cessation of stubble burning. i.

3.3. Improving scientific and staff support Most of the 56 projects of the National Research Program for 2016–2020 Soil resources: forecast of development, sustainable use and management aim to maintain or increase SOC stocks, for instance by cutting the share of row crops in rotations; use of optimal doses and technologies of mineral and organo-mineral fertilizers; zero tillage; application of crop residues, by-products and other organic wastes; and ploughing-in green manure. Unfortunately, this Program receives limited government

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funding so, to increase the potential of sustainable management of SOC, the following projects are proposed: 3.3.1. International projects i.

International projects on soil carbon monitoring and dissemination of technologies for increasing SOC, including incentives for farmers; ii. Implementation in Ukraine of the International Pilot Project for low-cost soil carbon monitoring on Chernozem using spectrometric equipment and technology of the Dutch company SoilCares; iii. International training seminars on SOC monitoring, e.g. the Ukrainian experience in organizing regular agrochemical field surveys; iv. Extension of the EU LUCAS topsoil survey (Baliuk and others 2017). 3.3.2. National projects with external financial and technical support i.

Demonstration and dissemination of technologies to improve the humus content in Ukrainian Chernozem and arresting soil degradation; ii. Assessment of SOC loss due to soil sealing; iii. Development of theoretical and practical models of financial incentives for SOC maintenance. 4. Expected results of implementation of the conception, determination of its efficacy The expected result of sustainable management of SOC is the achievement of stable SOC content by 2020 and a gradual increase by no less than 0.1% by 2030. This will play an important role in mitigating climate change and sustainable agricultural development. The key expected effects will be: Environmental: The estimated reduction of CO2 eq could reach 10.9 million tonne by 2030, representing 34% of the total CO2 eq emissions by Ukrainian agriculture. This may be translated to a carbon income of $US 220 million and a conditional rate of return of 4.28 in 2030. ii. Ecological and economic: The increased humus content of the soil (0.65 t/ha) from the use of organic fertilizers translates to a gain of potential soil fertility assessed nationwide at $US1778.4 million ($US144/tonne humus). iii. Economic: The potential additional gross harvest is 6.1 million tonne of grain bringing an additional income of $US915 million; an additional profit of $US153 million. i.

5. Investment needed to implement the concept The main cost is for organic fertilizers—$US1380.2 million per year: 57% for extraction and use of sapropel; 28% for traditional manure. We anticipate that this cost will be met by the landowners and land users themselves (Kucher 2015a). Potential sources of financing are detailed by Kucher (2015b).

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Conceptual framework of the project approach to implementation of sustainable management of SOC The concept has to be implemented through projects, i.e. sets of interrelated activities aimed at achieving specified goals within a limited time and with limited financial and other resources. The management of these projects is a project in itself and Fig. 1.3, developed from our previous studies (Kucher 2016b; Kucher and others 2018), is a conceptual framework for this project approach. It defines the purpose, subject, object, basic conceptual positions, principles, functions, methods, tools, components of the various systems in operation, and the place of economic management of these projects in the economic management of agricultural enterprises. The fundamental ideas are to orientate all decisions on intensification of production towards soil protection and conservation, and a shift towards strategic management.

Conclusions 1. Analysis of the current state and trends in the weighted average humus content of arable soils in Ukraine indicates, in general, a worsening situation although a few regions show improvement. 2. The total average annual economic damage from the loss of soil organic carbon in Ukraine over the past 50 years amounted to $US 986 million (25.2 bln UAH). 3. Sustainable management of SOC is a foundation for handling, or even solving, critical issues that include land degradation, sustainable agriculture and climate change. This study defines the mission, strategic goals, indicators and the main measures to accomplish the mission. The logical-semantic model consists of the goal block, the subject-object block, the information block, the organizationaland-technological block and the result block. We define the management directions needed to maintain/increase SOC stocks and arrest land degradation, and propose actions to increase management capacity. 4. The implementation of whole concept should increase the efficacy of management of SOC and arrest land degradation. The expected results of implementation up to 2030: (i) environmental effect—10.94 million tonne reduction of CO2 eq emissions, a carbon income for Ukraine of the order of $US 220 million; (ii) ecological and economic effect—the forecast annual benefit of the application of organic fertilizers on soil fertility nationwide is $US 1778 million; (iii) economic effect—the projected additional profit may amount to $US153 million.

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Fig. 1.3 Conceptual framework of the project approach to the implementation of the conception of sustainable management of SOC in agricultural enterprises

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Chapter 2

Status and Problems of Normative Monetary Valuation of Land in Ukraine Igor Iatsuk , Yuriy Dmytruk , Vasyl Cherlinka , and David Dent

Abstract Soil maps in Ukraine need to be updated as a matter of urgency. They are now more than 30 years old, many are 60 years old. The original mapping was undertaken for other purposes than today’s, and the soils have changed. Apart from all this, systematic errors have been introduced by digitization, geodesic rectification and the attribution of soil characteristics from the original mapping. Legal issues now arise at the point of sale of land on the basis of doubtful data, for instance with legalization of forecast agro-production groups or soils. This is neither fair nor reasonable. Prior to some future programme of large-scale soil survey, these problems may be avoided by deleting doubtful data and substituting the standard rental income of the corresponding agricultural and non-agricultural areas. Keywords Normative monetary valuation · Soil maps · Agro-production soil groups · Predictors · Errors

Introduction Rational and optimal use of the most important of productive resources demands economic valuation of the quality of farmland. It is self-evident that comprehensive information on the soil is needed for productive farming, environmental monitoring and management, and reliable monetary valuation of the land. Economic assessment should both determine its value and stimulate its efficient use; the productivity of the land and the cost of agricultural commodities depend on this efficiency. Soil quality monitoring, and not only of the topsoil, is needed to assess the impact of both natural

I. Iatsuk Institute of Soil Conservation of Ukraine, Babushkina, 3, korpus 3, Kyiv 03190, Ukraine Y. Dmytruk (B) · V. Cherlinka Yuriy Fedkovych Chernivtsi National University, Kotsyubynsky 2, Chernivtsi 58012, Ukraine e-mail: [email protected] D. Dent Chestnut Tree Farm, Forncett End, Norfolk NR16 1HT, UK © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Dmytruk and D. Dent (eds.), Soils Under Stress, https://doi.org/10.1007/978-3-030-68394-8_2

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and man-made environmental changes—more especially where the land is subject to degradation (Connellan 2004; Ivasenko 2008; Reynolds and others 2013). In Ukraine, about 15 million hectares (about one-quarter of the country) has never been assessed by systematic soil surveys; this includes the Carpathian and Crimean mountains, forest and built-up areas (Achasov and others 2015; Cherlinka 2017a). Large-scale soil mapping materials (LSSM), by which we mean maps of agro-production soil groups (APSG), are urgently needed for both monetary valuation and justification of soil treatment, fertilizers and protection against degradation. The available medium-scale soil maps are not up to the job. Field mapping in 1957– 1961 was intended, as quickly as possible, to make a provisional map of the soil cover at a scale of 1:25,000. Between 1969 and 1991, a second round of soil survey was undertaken at a field scale of 1:10,000. Although dubbed correction of soil survey materials, it was actually a completely independent survey on a fundamentally different plan. In particular, a new system of APSG was used which, unlike the previous surveys, applied a common legend across the whole country. This survey was completed across almost 85% of farmland but, since 1991, surveys have been only fragmentary (Kanash 2013). Ukraine is not alone. Belgium and Czechoslovakia achieved national coverage at 1:10,000 or better—the latter focussed on agricultural production as a basis for mapping Bonitat Soil Units (Džatko 1976)—but no other country has completed a large-scale soil survey. The problem is worldwide and there are no satisfactory solutions (e.g. Bui and Moran 2003, Dent and Dalal-Clayton 2014). Even in the USA, where the well-thumbed Soil Survey Geographic Database contains hundreds of estimated properties for soil landscapes and components, there are approximately 350million acres without soil maps. The National Soil Survey Center is still actively supporting soil surveys and other soil investigations, not least by correlating every soil map component to ecological sites and building state and transition diagrams for each ecological site (Dan Hoover personal communication). So, we all rely on archival data. These present several problems besides obsolescence: data gaps, storage quality, compatibility with digital soil mapping and copyright issues (Omuto and others 2013, Cherlinka and Dmytruk 2014). In Ukraine, Normative Monetary Valuation (NMV) of agricultural land is a prerequisite for determining rents, land tax, state duty and other mandatory payments, so there has been intensive work to prepare an approved procedure (Cabinet of Ministers 2018a, b). It was envisaged that consolidation of all necessary data and creation of an NMV portal would enable on-line calculation, which is considered desirable in the interests of speed and objectivity (State Geocadastre of Ukraine 2018). The basis of this valuation is information on the APSG (also known as agro-industrial soil groups). A priori, it is assumed that such data are reliable. Unfortunately, there a serious, systematic problem (Achasov and others 2019; Cherlinka and Dmytruk 2018a, b; Dmytruk and Stuzhuk 2017) and the NMV portal doesn’t provide any opportunity for interaction by independent experts, including downloading fundamental geospatial data. This is in contrast to the situation in the USA where there is comprehensive access to the information provided: soil data with support from the USDA Natural Resources Conservation Service (NRCS 2018), and

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a complete set of Earth Remote Sensing data and digital terrain models with assistance from the US Geological Survey (USGS 2018). Thus, in the absence of the law itself, until now only in draft (Cabinet of Ministers 2009), a fully functional modern geo-information environment has been held back and a whole generation of technological solutions developed in various fields (soil science, cadastre, territorial management) is not implemented. Only a pilot project for the development of geospatial data structure has been announced (State Geocadastre of Ukraine and JICA 2015), wherein it was planned to collect and consolidate information on the boundaries of administrative units, topography and drainage, buildings and structures, transport and communications networks, vegetation and soil, cadastre, geographical names and the State Geodetic Network. The prototype National Geospatial Data Infrastructure (State Geocadastre of Ukraine 2018) declares that all data and their combinations are available on-line. However, analysis of the presented results shows that not all data are available. As of now, NMV portal provides only limited opportunities to establish some of the issues of the quality of APSG maps and, accordingly, the NMV of agricultural lands.

Materials and Methods Only farmland has been surveyed, and not all of it. Settlements, forests and uplands are left without information, and the soil surveys made in the period 1957–1961 are out of date; there are many irrelevant data, and ‘corrections’ performed over the next quarter century were only partial (Cherlinka 2017b, c, d, e). To consider the status and existing problems of NMV of agricultural land related to discrepancies in the mapping of APSG in Ukraine, we have investigated the situation in Lanovets District of Ternopil region (Fig. 2.1), located within the Western Ukrainian province of the Forest-Steppe zone. It is situated on the eastern border of the Ternopil region, adjoining three districts of Khmelnytskyi Region (Belogorsk, Teofipol and Volochysk) and, within Ternopil region, adjoins Shumsk, Zbarazh and Pidvolochysk districts. Lanovets District, itself, consists of 27 administrative districts and covers an area of 632 km2 (Fig. 2.1c). Its contrasting landforms make for a complicated soil pattern. We have combined systematic and comparative analysis, a combination of historical and logical analysis and synthesis, mathematical modelling and variational statistics; we have also introduced some advances in construction, analysis and application of digital elevation models (DEMs) in soil science. Open access software tools were used to process the data: georectification of mapping material, digitization, mapping of morphometric parameters and construction of a predictive model of soil cover (QGIS Development Team 2015; EasyTrace Group 2015; GRASS Development Team 2017; R Development Core Team 2017). In order to process large areas, the coordinate system was brought to a planar Albers projection with the following parameters (project 4, Evenden and Warmerdam 1990):

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Fig. 2.1 Location of the study area within Ukraine (a), inside Ternopil and Khmelnytskyi Regions (b) and test site position (plotted on OpenStreetMaps)

+ proj = aea + lat_l = 46 + lat_2 = 51 + lat_0 = 48 + lon_0 = 32 + x_0 = 0 + y_0 = 0 + ellps = krass + towgs84 = 23.92,141.27,80.9,0,0.35,0.82,0.12 + units = m + no_defs.

According to the Decree of the Cabinet of Ministers (2018b), natural and agricultural zoning of lands of Ukraine was conducted and a map of natural and agricultural zones of Ukraine was produced at a scale of 1:500,000 distinguishing natural taxonomic divisions. The resolution envisages that natural-agricultural zoning will be

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the basis for the calculation of the normative monetary evaluation of the farmlands and state planning for the rational use and effective protection of natural resources (MAPFU 2019). But all these will require information at a larger scale than one to half a million.

Results Our analysis reveals several problems with LSSM. One is the mismatch of soil and APSG mapping units at the boundaries of the former collective and state farms (Fig. 2.2b), each of which was the responsibility of a different survey team. There are several reasons for this but a solution is obviously needed at the national level— especially now that Ukraine is introducing a land market. After consolidating the APSG nomenclature to a single system (3599 individual APSG), we digitized the information and obtained preliminary data on the coverage of Lanovets District by soil surveys: 142.16km2 out of 632.37 km2 (22.5%) is completely unavailable (Fig. 2.2a), in addition to un-surveyed settlements, forests and upland. APSG contour information enables calculation of the lengths of polygon boundary that are either without soil information, absolutely identical or completely mismatched (Fig. 2.2b). We conducted two cycles of calculation to assess the coincidence of external boundaries (Fig. 2.3a) and internal boundaries (Fig. 2.3b). Ultimately, 46% of the APSG boundaries are completely inconsistent and only 22% are fully matched (Fig. 2.3c). Considering this situation along with the kinds of soil differences at the boundaries, we may conclude that a similar proportion of ~50% would be observed if data were available for the unsurveyed 142.16 km2 . In view of

Fig. 2.2 Analysis of APSG across Lanovets District: a availability of soil data; b quality of matching of APSG boundaries

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Fig. 2.3 Degree of coincidence of external (a), intra (b) and aggregate (c) boundaries of APSG

the results for this exemplar area, it is possible to approximate the situation countrywide and forecast the extent of compliance of NMV of agricultural land to the real situation.

Discussion Soil survey calls for a good field eye, knowledge of the land and, in particular, expert knowledge of the soils of the area in question. There are always too many acres and too few surveyors, so never enough observations; subjective judgements are made; sheet boundaries between surveys by different teams rarely coincide and have to be settled by correlation. Given that this was achieved 60 or 30 years ago, there would still be mismatches between soil surveys of different periods and, subsequently, differences between soil mapping units and APSG resulting from erroneous integration of soils into these groups. None of this can be investigated without the originals of the soil maps themselves. Further causes of the revealed unsatisfactory situation are analysed by Cherlinka and Lobova (2018). Only a part of the LSSM, including the cartograms of APSG, has been translated into electronic format, but not the soil maps on which they are based. Questions arise about geo-rectification of APSG cartograms: in the absence of a geodesic grid, geo-rectification throughout Ukraine was made on orthophotos using different sets and variable numbers of tie points, transformation algorithms, etc. As a result, the areas and boundaries of the APSG were shifted relative to the natural (and not only natural) objects to which they are confined (Cherlinka 2015). Furthermore, the modern orthophoto plan may differ from that of 30–60 years ago because of changes in the outlines of forests, rivers, displacement of roads, especially field tracks, and so on. Moreover, the implementation of this project (Loan Agreement 2003) envisaged separate digitization of individual APSG cartograms according to local councils and settlements. Errors in geo-rectification spawned multiple variants of boundaries that,

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often, did not coincide, so the cartogram of agricultural groups was arbitrarily drawn under the boundaries of settlements/village councils, bringing a cascade of additional errors due to the multiplicity of fragmented polygons, gaps between them and overlays. Bearing in mind how the original soil maps were made, the boundaries of collective and state farms should have been considered (the coordinates of the key points of these boundaries were available and geo-rectification to these points could potentially have given the best outcome) but this course was not attempted. Instead, the distribution of parts of the farms into separate administrative units fragmented the integral APSG into separate village council units before geo-rectification was undertaken. Even when the cartograms were not separated, geo-rectification by different operators created discrepancies in the raster data within the council’s boundary as well as between the council’s boundaries and APSG cartograms. There were also attribution errors from the introduction of soil mapping units instead of APSG codes, or different variations of alphabetic indices describing the soil texture, or a mixture of characters in different encodings and keyboard layouts… All these maps were summarized, geo-rectified and processed within a very short time (about 4 months). In paying tribute to this achievement, we believe that some errors need to be and can be corrected. Correcting individual errors, such as the mismatching of the boundaries of APSG (or soils in the case of soil maps) at the boundaries of administrative entities, is impossible without a large-scale soil survey. This is unlikely in the foreseeable future. Cherlinka and Lobova (2018) propose the use of several kinds of buffer zones but this approach does not solve the problems associated with the legal status of manifest errors within the administrative boundaries. The proposed amendments differ in the degree of modification of the original data but, essentially, all original but doubtful APSG are completely removed and the resulting blanks filled with forecast data according to the tried-and-tested method (Cherlinka 2017b, c, d, Cherlinka and others 2017). In short: (a) construction of a DEM; (b) digitization of cartographic materials and assessment of their quality; (c) analysis of DEM and generation of a set of maps of morphometric and other derivative characteristics in GIS GRASS (values and aspect of slope, surface curvatures (longitudinal and maximum), amount of solar radiation, landforms, topographic wetness index, accumulation, direction and length of water flows and distance to them); (d) assessment of the closeness of the relationship and the role of these parameters in the variability of the soil cover; e) creation of a predicative soil or APSG map (map version or map model) using 14 types of predicative algorithms both for areas with an existing soil (APSG) map and for those where there is none. To create simulation models of APSG cartograms, we used our own script in R-statistics language (R Development Core Team 2017). For statistical estimation of quality of the models, we used the Cohen’s Kappa Index (Landis and Koch 1977), and we usually use predicative algorithms like multinomial and penalized logistic regressions, neural networks, decision and bagged trees, random forests, naive Bayes, K-nearest neighbours, partial least squares, flexible non- and linear discriminant analysis, support vector machines and nearest shrunken centroids. However, trade-offs have to be made. If doubtful groups occupy a lot of an administrative area, then removing this information also removes a lot of potentially useful

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information and significantly increases the area filled with forecast data. With the complete fallout of some APSGs in the simulation, there will be significant errors on the forecast map. A way out of this situation is not to completely remove the APSG but make up a buffer zone along the original borders of farms. The width of such an area will depend on the complexity of the soil landscape. N.B. our previous research warns that, although the accuracy of the model prediction is high, the model inherits all the flaws of the original maps. At the end of the day, we should also consider the position of our legal colleagues, and, to avoid legal issues with the predicted APSG or soils, the doubtful APSG must be completely removed. For these areas, the standard of the capitalized rent income on agricultural lands of the appropriate agricultural areas or, as the case may be, cities may be applied (Cabinet of Ministers 2018a, b). This approach will help to reduce social tension and the number of legal conflicts arising from the different diagnosis of soils of the same kind but mistakenly defined and already legalized on the cartograms of APSG.

Conclusions A big share of the gross national product of Ukraine comes from highly productive soils so there need for high-quality normative monetary valuation of land. This is impossible without taking into account the particularities of the soil data that underlie such assessment. The soil survey information across the country is in urgent need of updating to meet current requirements. Systematic errors in the transformation of the original survey data into APSG cartograms cause many problems; in particular, the sale of agricultural land on the basis of unreliable data on the soils is neither reasonable nor fair. Prior to essential re-survey, to implement a more reliable NMV and avoid legal problems with the legalization of unsubstantiated information on APSG or soils, doubtful APSG data should be completely removed. For these sites, the standard of the rented income of the respective agricultural land of the natural and agricultural area is applied in accordance with the NMV. Acknowledgements The programming and mathematical modelling was partially funded by the National Scholarship Program supporting the mobility of students, PhD students, university teachers, researchers and artists established by the approval of the Government of the Slovak Republic [2016/2017:id17680]. NSP is funded by the Ministry of Education, Science, Research and Sport of the Slovak Republic. The programme is managed by SAIA, n. o. (Slovak Academic Information Agency).

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References Achasov, A.B., A.A. Achasova, and A.V. Titenko. 2019. Soil erosion by assessing hydrothermal conditions of its formation. Global Journal of Environmental Science and Management 5 (SI), 12–21. https://doi.org/10.22034/gjesm.2019.SI.02. Achasov, A.B., H.V. Titenko, and V.I. Kurilov. 2015. Remote sensing data as a basis of mapping soils: Economic analysis. Visnyk of V.N. Karazin Kharkiv National University. Series Ecology 1104 (10): 60–66 (Ukrainian). Bui, E.N., and C.J. Moran. 2003. A strategy to fill gaps in soil survey over large spatial extents: An example from the Murray Darling basin of Australia. Geoderma 111 (1): 21–44. Cherlinka, V.R. 2015. Adaptation of large-scale maps of soils to their practical use in GIS. Agrochemistry and Soil Science. Collected Papers. ISSAR, Kharkiv 84: 20–28 (Ukrainian). Cherlinka, V.R. 2017a. Using geostatistics, DEM and remote sensing to clarify soil cover maps of Ukraine. In Soil science working for a living. Applications of soil science to present day problems, ed. D.L. Dent and Y. Dmytruk, 89–100. Cham: Springer Nature. https://doi.org/10.1007/978-3319-45417-7. Cherlinka, V.R. 2017b. Influence of resolution of digital elevation models on the quality of predicative simulation of soil cover. Gruntoznavstvo 18 (1–2): 79–95. https://doi.org/10.15421/041709 (Ukrainian) . Cherlinka, V.R. 2017c. Morphometric parameters of relief as basis for predictive modelling of spatial distribution of soil cover. Agrochemistry and Soil Science. Collected Papers. ISSAR, Kharkiv 86: 5–16 (Ukrainian). Cherlinka, V.R. 2017d. Variations in the predictive efficiency of soil maps depending on the methods of constructing training samples of predicative algorithms. Ecology and Noospherology 28 (3–4): 55–71. https://doi.org/10.15421/031716 (Ukrainian). Cherlinka, V.R. 2017e. Ways to overcome the quality crisis of soil mapping materials. In IV All-Ukrainian Scientific-Practical Conference:National Scientific Space: Perspectives, Innovations, Technologies, November 3–4, 2017, 61–63. Scientific Partnership Centre for Scientific Technologies (Ukrainian). Cherlinka, V.R., and Y.M. Dmytruk. 2014. Problems in creating, georectifications and use of large scale digital elevation models. Geopolitics and Ecogeodynamics of Regions 10 (1): 239–244 (Ukrainian). Cherlinka, V.R., and Y.M. Dmytruk. 2018a. Solving existing problems with soil maps in Ukraine. Biological Systems 10 (l): 298–308. https://doi.org/10.31861/biosystems2018.01.094 (Ukrainian). Cherlinka, V.R., and Y.M. Dmytruk. 2018b. Verification methods for predicative soil maps. Biological Systems: Theory and Innovation 287: 159–172. https://doi.org/10.31548/biologiya2018. 287.160 (Ukrainian). Cherlinka, V.R., Y.M. Dmytruk, and V.S. Zaharovskii. 2017. Comparative estimation of the accuracy of simulation modelling of soil cover and forecast of cartograms of agro-production groups. Biological Systems 9 (2): 298–306. Cherlinka, V.R., and O.V. Lobova. 2018. Methodological approaches to coordination of soil cartographic materials on the borders of administrative territorial units of Ukraine. Scientific Reports of NUBiP of Ukraine 6 (76): 1–15. Cabinet of Ministers. 2009. On the national geospatial data infrastructure. Draft Law of Ukraine of Ukraine No. 5407 dated 03.12.2009 (Ukrainian). Cabinet of Ministers. 2018a. On approval of the Methodology for normative monetary valuation of the agricultural land. Decree No. 831 dated 16.11.2016 (Ukrainian). Cabinet of Ministers. 2018b. On conducting the national (all-Ukrainian) normative monetary valuation of the agricultural land and amending some resolutions of the Cabinet of Ministers of Ukraine. Decree No 105 dated 07.02.2018 (Ukrainian). Connellan, O. 2004. Land value taxation in Britain: Experience and opportunities. Cambridge, MA: Lincoln Institute of Land Policy.

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Dent, D.L., and B.D. Dalal-Clayton. 2014. Land resources information in dire straits. Agriculture for Development 23 (3): 20–23. Dmytruk, Y., and O. Stuzhuk. 2017. Making better soil maps using models of tangential curvature. In Soil science working for a living. Applications of soil science to present-day problems, ed. D.L. Dent and Y. Dmytruk, 101–107. Cham: Springer Nature. https://doi.org/10.1007/978-3-319-454 17-7_8. Džatko, M. 1976. Charakteristika bonitovaných pôdnoekologických jednotiek SSR: Metodická príruˇcka na využitie máp BPEJ v praxi. Bratislava: Priroda (Slovak). EasyTrace group. 2015. Easy Trace 7.99 Digitizing software. Evenden, G., and F. Warmerdam. 1990. Proj 4—Cartographic Projections Library. Source code and documentation. Open Source Geospatial Foundation. https://proj4.org/. GRASS Development Team. 2017. Geographic Resources Analysis Support System (GRASS GIS) Software. Version 7.2. Ivasenko, A.G. 2008. Foreign experience of cost estimation of agricultural setting land. Vestnyk UHTU-UPY. Economics and Management Series 4, 80–85 (Russian). Kanash. O.P. 2013. Soils as a leading component of land resources. Land Management and Cadastre 2, 68–76 (Ukrainian). Landis, J.R., and G.G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33 (1): 159–174. https://doi.org/10.2307/2529310. Loan Agreement between Ukraine and the International Bank for Reconstruction and Development. 2003. Project “Issuance of State Acts on Land Ownership in Rural Territory and Development of the Cadastre System” dated October 17, 2003. Document 996043 (Ukrainian). MAPFU. 2019. Portal of normative monetary valuation of land plots. Ministry of Agrarian Policy and Food of Ukraine. NRCS. 2018. Web Soil Survey. USDA Natural Resources Conservation Service. Omuto, C., F. Nachtergaele, R.V. Rojas, and others 2013. State-of-the-art report on global and regional soil information. Where are we? Where to go? Food and Agriculture Organization of the United Nations, Rome. QGIS Development Team. 2015. QGIS Geographic Information System. R Development Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Reynolds, B., P.M. Chamberlain, J. Poskitt, and others. 2013. Countryside survey: National soil change 1978–2007 for topsoils in Great Britain—Acidity, carbon, and total nitrogen status. Vadose Zone Journal 12 (2): 1–15. State Geocadastre of Ukraine. 2018. Prototype of national geospatial data infrastructure in Ukraine. February 22, 2018 (Ukrainian). State Geocadastre of Ukraine and Japan International Cooperation Agency (JICA). 2015. Japan and Ukraine launch two-year project within National Geospatial Data Infrastructure. Kyiv (Ukrainian). US Geological Survey. 2018. EarthExplorer

Chapter 3

An Investable Proposal to Transform the Steppe David Dent, Boris Boincean, and Zhanguo Bai

Abstract Soil is the biggest brake on global heating. It holds more carbon than the atmosphere and all standing vegetation put together. But farmers have been burning off soil organic matter for 12 thousand years; the last thirty-five years of satellite measurements of carbon-capture capacity reveal a dramatic decline across the steppes; the best soil in the world is the worst example of land degradation. Long-term field experiments show that, since 1970, soil carbon has been run down by 165–192 tC/ha; taking the least of these figures, mineralization of soil carbon has emitted 25 ppm of atmospheric CO2 over this period. To turn this situation around: stop ploughing; don’t fallow; plant windbreaks; adopt a diverse cropping system; and integrate crops and livestock—alternatively, convert the biomass to biogas. In Ukraine, this plan, with one year in three under perennial legumes and grasses, would transform the annual loss of 3.4–4 tC/ha to annual carbon capture of 0.5– 1.5 tC/ha/yr, save half the cost of diesel fuel and increase production. At the same time, the legumes will produce all the nitrogen the crops can use—with enormous savings in the energy presently used to manufacture nitrogen fertilizer. Integration of crops with livestock would transform the rural economy and the green biomass, converted to biogas, could replace the country’s coal-fired power stations. Keywords Black Earth · Carbon sequestration · Biogas · Policy · Investment

The Black Earth This is a proposal for regenerative agriculture on the best soil in the world: the Black Earth of the steppes which we may take to include Chernozem (Fig. 3.1); Phaeozem, D. Dent (B) Chestnut Tree Farm, Forncett End, Norfolk NR16 1HT, UK B. Boincean Selectia Research Institute of Field Crops, Alecu Russo B˘al¸ti State University, Calea Iesilor 28, 3101 B˘al¸ti, Republic of Moldova Z. Bai ISRIC—World Soil Information, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Dmytruk and D. Dent (eds.), Soils Under Stress, https://doi.org/10.1007/978-3-030-68394-8_3

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Fig. 3.1 Typical chernozem. When this photo was taken, 50 years ago, the soil held 585 tonne of organic matter (340 tC) per hectare in the top metre and half as much again below that depth

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Fig. 3.2 World map of Black Earths

which is much the same except it lacks free carbonates; and their dryland cousin the Chestnut Earth or Kastanozem which has a thinner topsoil with a lesser humus content, and both carbonates and gypsum in the subsoil. Black Earths occupy a tract of land across the steppes from Eastern Europe to China and, also, across the Prairies of North America and the Pampas of South America (Fig. 3.2). The topsoil of the Chernozem is a metre thick. It was created by perennial grassland, grazing animals, invertebrates and myriad microorganisms, all dependent on each other. The keystone species are perennial grasses. Their roots are responsible for most of the soil organic matter and the unique, granular structure—the size of garden peas—that can absorb all the rainfall and snow melt that come its way and supply crops with water through the long dry summer. The soil organic matter, itself, is a rich reserve of plant nutrients. It has been treated as if it were inexhaustible—but it’s not. And this is the focus of this proposal.

The Need for Conservation Agriculture Black Earth is a special case of a broader issue: soil is the biggest brake on global heating; it holds five times as much carbon as the atmosphere, more than the atmosphere and all standing vegetation put together. But farmers have been burning off soil organic matter for 12 thousand years. They are responsible for one-third of greenhouse gas emissions. They have run up an enormous carbon debt; the better the soil, the bigger the debt; the best thing they could do for the planet is to put it back again. Where better to start than the best soil in the world? Half of the organic matter that makes Black Earth what it is has been pumped into the air and 35 years of satellite measurements reveal a dramatic decline in carbon-capture capacity across the steppes (Fig. 3.3).

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Fig. 3.3 Trends of carbon-capture capacity across the steppes (Bai and others 2015) that translates to a loss of primary productivity of as much as 50 kg/ha/year

The best soil in the world is the worst example of land degradation. And not for the first time. In the 1930s, three-quarters of the topsoil and three and a half million Americans left the Dust Bowl on the Great Plains. After that, soil conservation measures were developed: contour bunds, grassed waterways and the like. They were never popular because of their initial cost and continual upkeep. And they don’t deal with the root cause of soil erosion: which is bare soil. Annual crops give scant protection against the elements, and ploughing trashes soil structure and accelerates the loss of humus, erosive runoff and loss of topsoil in dust clouds. Conservation Agriculture (CA) does different. It embraces zero tillage, continuous ground cover by crops or crop residues, and diverse crop rotations that control weeds, pests and diseases, include perennial legumes that substitute for nitrogen fertilizers, and that return green biomass to the soil (Madrid Declaration 2001). It works everywhere for the simple reason that it eliminates destructive tillage and the daily attacks of sun, wind and rain. The purpose of ploughing is to kill weeds. Desiccant herbicides, in particular glyphosate, made zero tillage a viable proposition, offering arrest of soil erosion, more reliable yields, more planting days, less outlay on-farm machinery, and 70% less fuel consumption and man-hours.

Greenhouse Gas Emissions The pre-industrial concentration of CO2 in the atmosphere was 280 ppm. Burning fossil fuels and farming have boosted it beyond 400 ppm. This is forcing global

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heating. To hold global heating to 1.5 °C above pre-industrial levels, emissions must be halved by 2030, eliminated by 2050 and, then, the excess greenhouse gases must be hauled back (IPCC 2018). But instead of absorbing CO2 , agriculture is pumping it out: more soil organic carbon is being lost by mineralization than is being put back again. Since 1970, soil carbon across the steppes has been run down by 165– 192 tC/ha (Boincean and Dent 2019). Taking the least of these figures, Liu’s estimate of the area of Black Soils in Eurasia (3.23 million km2 , Liu and others 2012) and assuming that two-thirds of them are under the plough, mineralization of soil carbon has emitted (195 Gt or 25 ppm of atmospheric CO2 since 1970). Table 3.1 details the position in Russia, Ukraine, Kazakhstan and Moldova. Emissions from SOC can be halved by eliminating fallow. The whole CA package offers: • Carbon capture of 0.5–1.5 Gt/ha/yr. In Ukraine, CA with one year in three under perennial legumes and grasses would transform the annual loss of 3.4–4 tC/ha to annual carbon capture of 0.5–1.5 t soil organic carbon/ha/yr. At the same time, the legumes will produce all the nitrogen the crops can use—with enormous savings in the energy presently used to manufacture nitrogen fertilizer. • Arrest of soil erosion • Bigger crops. The effect of crop rotation is an extra tonne/ha yield of winter wheat, so present production can be achieved from a lesser area, making room for the perennial grasses and legumes that we need to put the organic matter back into the soil. Current commercial wheat yields are about 2 t/ha; the effect of crop rotation will make up for the loss of one-third of the sown area of cereals; for Ukraine, doubling the effect of crop rotation by application of farmyard manure will increase production by about 9 million tonne over present levels (half of present grain exports). Table 3.1 Potential of CA to cut atmospheric CO2 and raise crop yields Country

Total Area area of K km2 Black Earth K km2

Arable Black Earth K km2

Area of Chestnut Earth K km2

Arable CO2 emissions Chestnut 1970–2020 Gt Earth (ppm) K km2

Double cereal yield from 0.66 area, million t

Russia

17,098 1348

1078

394

177

75.9 (9.7)

+ 27.3 50

Ukrainea

603

233

13

11

14.8 (1.9)

+ 9.1

265

Area under CA in 2015/6 K km2

7

Kazakhstanb

2725

212

126

855

132

15.6 (2.0)

+ 4.2

27

Moldova

34

27

22

nil

nil

1.3 (0.2)

+ 0.6

0.6

1 Gt = 1000 million tonne; 1 ppm atmospheric CO2 = 7.8 Gt a Baliuk and others 2015; b Govt Kazakhstan 2019

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At the moment, there’s no market for the green biomass. In the Agricultural Revolution of the eighteenth century, the answer was to feed it to livestock that turn it into meat, milk, wool and manure. Integrating crops and livestock will regenerate rural communities and the extra production will make space to restore degraded land for wildlife, water resources and amenity. But the people and skills needed to manage livestock are now hard to come by. The alternative is to turn the green biomass into biogas. Biomass, converted to biogas, could replace the country’s coal-fired power stations,1 and the digestate from biogas production is first-class organic fertilizer that will recycle all the plant nutrients and most of the organic matter.

Critical Issues of Ownership and Investment CA has been adopted over 15% of the world’s cropland: 35% in North America, 65% in South America, 75% in Australia, but less than 10 million ha across the steppes. Investment in know-how and infrastructure would speed things up but there are two outstanding issues. The first is ownership of the problem, which is related to ownership of the land. The people who farm the land don’t own the land. They may hire it but slavery is a better analogy; they take no responsibility—it is an expendable source of income. We might expect that the development of a land market will extend ownership of land to the users of land and, with it, responsibility for its health—but this issue is surely the responsibility of the legislators of the country. Secondly, we need a new means of finance. The 2015 Paris Accord was forced on governments by the world’s bankers and insurers. Corporate reporting on how companies are accounting for climate change reveals risks assessed in $trillions. Capital investment of $120 trillions has been lined up to counter these risks, and the cheapest finance available is through Green Bonds issued by governments or municipalities that have the capacity to accomplish the work. Every bond offered has been oversubscribed. The shortfall is of credible action plans, so here is an investable 5-year plan for the steppes that will save gigatonnes of emissions every year (0.3 Gt from Ukraine) by cutting the mineralization of soil organic matter. It will save as much again from direct energy savings in the agriculture sector and will draw down a further 0.5–1.5 Gt/yr. 1 In

1990, Ukraine devoted 1.4 million ha to leguminous forage, producing 46.5 million tonne of green mass. Following the collapse of the Soviet Union, the area under legumes fell dramatically along with the decline of livestock. Sustainability requires about one-third of the sown area under perennial grasses and legumes. In Ukraine, that is about 5 million ha which will produce 163.3 million tonne green biomass. Conversion of green press cake (80% water) to biogas is 110 m3 gas/tonne, which will yield 1.8 billion m3 of biogas that can be injected into the gas grid or used to generate electricity on site. It equates to 178 million MWh of electricity as well as an equivalent amount of heat. To produce 178 million MWh electricity needs a total installed power capacity of 24,000 MW, much the same as Ukraine’s coal-fired power stations. This is six times the present installed biogaselectricity capacity in Germany where, in 2015, 8726 biogas plants were in production with a total capacity of 3905 MW.

3 An Investable Proposal to Transform the Steppe

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5-year Action Plan for the Steppes 1. Stop ploughing 2. Don’t fallow. Instead, plant a cover crop like annual medic or a mixture of perennial legumes and grasses that can be undersown with the main summer crop. 3. Adopt a diverse cropping system. This is crucial. Crop rotations that include cover crops, perennial legumes and grasses control weeds, pests and diseases without resort to toxic chemicals and, at the same time, generate enough roots to replace the humus lost by mineralization. 4. Integrate crops and livestock. Alternatively, convert the biomass to biogas. 5. Plant windbreaks against a drying climate.

How Much Will It Cost? Re-equipment costs are manageable given that the costs of replacing machinery come around all too often. And less power will be needed. Countrywide adoption of no-till in Ukraine will require new machinery costing some $7.6 billion. Windbreaks should comprise 4% of the arable area. Ukraine needs about 1 million ha at a cost of about $2 billion—a big job but more than 2 million ha was planted in the European part of the Soviet Union between 1949 and 1953 under Stalin’s Plan for the Transformation of Nature. Smarter farmers: Farms have had to get bigger or go out of business. They have shed labour and adopted simplified farming systems that depend on ever-morepowerful machinery, more-potent pesticides and fertilizers, smarter irrigation and new crop varieties that can take advantage of the new technology. But CA demands smarter farmers. How much are they worth? Re-skilling of agriculture needs an extension service linked with on-farm research that will yield immediately extendable results. That means staff, training, facilities, communications and transport. For instance, each of the 26 oblasts needs a trained, fully equipped extension team, say half a dozen staff, and each will cost $500,000 annually (ten times the payroll figure if we include national coordination and on-farm research). Infrastructure for a livestock industry: Given the market demand, this should be self-financing. Biogas would be a strategic investment for Ukraine that presently depends on gas from Russia. The creation of a market for green biomass would obviate the need for other incentives. If we take a 500 KW biogas plant as a standard unit, the installed cost of each one will be about $2 million. For Ukraine, to phase out its present coalfired generating capacity of 24 GW, which has to be done in any case, will need 48 thousand 500 KW plants, costing $96 billion at today’s prices. This is the kind of money that Green Bonds have been created for. The finance is there for the asking. Don’t think small, it is futile. Think how big it could be, and double it!

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References Bai, Z.G., D.L. Dent, L. Olsson, and others. 2015. A longer, closer look at land degradation. Agriculture for Development 24 (1): 3–9. Baliuk, S., and others. 2015. Agrochemical service in the preservation and increase of soil fertility. Scientific report. Kyiv: Striped Printing House (Ukrainian). Boincean, B.P., and D.L. Dent. 2019. Farming the Black Earth. Cham: Springer Nature Switzerland. Govt Kazakhstan. 2019. Consolidated analytical report on the state and use of land in the Republic of Kazakhstan 2018. Ministry of Agriculture and Ministry of Information and Communications, Astana (Russian). IPCC. 2018. Report of intergovernmental panel on climate change. Incheon, Republic of Korea, October 8. Liu, X., C Lee Burras, Y.S. Kravchenko, and others. 2012. Overview of Mollisols in the world: Distribution, land use and management. Canadian Journal of Soil Science 92 (3): 383–402. Madrid Declaration. 2001. First World Congress on Conservation Agriculture, Madrid. European Conservation Agriculture Federation and FAO.

Part II

Pedology and Soil Survey

Field workers, principally soil surveyors, have a practical interest in soil development which is shared by no other group. The soil surveyor is constantly faced with the necessity of predicting soil occurrence. Obviously, the correctness and, therefore, the usefulness of the map, will depend not only on his skill in recognising a given combination of soil features but also on his ability to predict where this combination may occur. Guy Smith, 1941

Chapter 4

Creating Digital Elevation Models Using Budget Unmanned Aerial Vehicles Andrii Achasov, Arkadiy Siedov, Alla Achasova, Ganna Titenko, and Oleg Seliverstov

Abstract In a comparative analysis of the accuracy of digital elevation models (DEMs) constructed from data acquired by various middle-price unmanned aerial vehicles (UAVs), six different budget UAVs were used with eight different cameras at heights of 25, 50 and 100 m. DEM construction was carried out with reference to ground control points, and without such reference using the coordinates of the onboard GNSS receivers. The main indicators of DEM quality were elevation and plan mean square errors. The results varied: use of ground control points did not always improve the quality of the model but, even taking account of their discrepancies, they can be used to create topographic and thematic large-scale maps. Keywords UAV · Accuracy of DEM · Root mean square error · Soil mapping

Introduction Unmanned Aerial Vehicles (UAVs) have become commonplace. Their transition from costly, hand-built instruments to everyday appliances and children’s toys has been made quickly thanks to rapid technological progress and the huge market demand. The current proliferation of platforms, and the equipment they carry, raises a logical question for a wide variety of applications: What equipment is necessary and sufficient to solve specific theoretical and practical problems? The use of UAVs for environmental monitoring and agriculture (e.g. soil science, agricultural chemistry, crop production, crop breeding) has a short but rich history. They are used for soil mapping (Krenz and others 2019; Achasov and others 2019) and studying individual soil parameters (Aldana-Jague and others 2016; Ivushkin A. Achasov (B) · G. Titenko · O. Seliverstov VN Karazin Kharkiv National University, 4 Svobody Square, Kharkiv 61022, Ukraine A. Siedov VV Dokuchaev Kharkiv National Agrarian University, Dokuchaevske-2, Kharkiv 62483, Ukraine A. Achasova AN Sokolovsky Institute of Soil Science and Agrochemistry, 4 Chaykovska St, Kharkiv 61024, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Dmytruk and D. Dent (eds.), Soils Under Stress, https://doi.org/10.1007/978-3-030-68394-8_4

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and others 2019; Ge and others 2019); vegetation and forest mapping (Dunford and others 2009; Dash and others 2017); environmental monitoring (Manfreda and others 2018); assessing the state of crops at various stages of development (van der Wal and others 2013; Achasov and others 2015; Franceschini and others 2017) and in precision farming (Huuskonen and Oksanen 2018; Zhang and Kovacs 2012); and in the quest for crop varieties adapted to climate change (Kunz and others 2020). One promising scientific application of UAVs is in the construction of digital elevation models (DEMs) by aerial photography (Glotiov and Gunina 2014). In turn, DEMs may be used to predict various soil attributes (Moore and others 1993; Gessler and others 2000; MacMillan and others 2003; De Menezes and others 2018) and, according to Bishop and Minasny (2005), nearly 80% of digital soil mapping projects use DEMs for forecasting local features of soil cover. Moreover, UAVs simultaneously provide information about soil colour, another feature traditionally employed in identifying the structure of soil cover (Achasov 2009). The ability to quickly obtain detailed DEMs with the help of UAVs, as a halfway house between satellite imagery and ground-based observations, enables monitoring of erosion processes and measurement of the volume of ravines and displaced soil (d’Oleire-Oltmanns and others 2012). Monitoring studies are often undertaken on a tight budget. Selection of equipment is one of the first stages of any investigation so the purpose of this article is a comparative assessment of the capabilities of modest-budget UAVs for the construction of DEMs to be used in large-scale soil mapping and monitoring.

Materials and Methods Studies were conducted at the Dokuchaevo landfill site in Kharkiv District: 49° 53’55.69 N, 36° 27’39.74 E (Fig. 4.1). The 5.3 ha site selected for testing the UAVs exhibits complex topography—a combination of a long slope with a stabilised ravine (Fig. 4. 2). The absolute heights of the investigated territory are 132.7–156.5 m, the difference 23.8 m. In the course of a geodetic survey, 14 reference points were established and marked with wooden pegs, their centres were combined with labelled white plastic discs that served to identify the points amongst the vegetation and soil cover. For each point, geographic coordinates and altitude were determined in duplicate using a Leica TCR 405 total station. The scatter of values of the horizontal dimension (plan) coordinates (X, Y) was ± 2–5 mm, and for the vertical (height-altitude) dimension (Z) ± 5–7 mm, which is an acceptable deviation given the terrain. Six different UAVs were used in the study, with 8 different cameras (Table 4.1). Flights were carried out at three altitudes: 25, 50, 100 m (Table 4.2). The flights were conducted on 24.05.17 and 16.06.7 in similar weather and time of day: cloud cover less than 3octas, wind speed up to 3 m/s, and at between 11.00 and 15.00 hours to minimise shadows and to take advantage of the most natural colour rendition.

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Fig. 4.1 Location of the landfill

Auto-flight routes were constructed at the chosen heights with 80% overlap on the photographs using Pix4D Capture mobile software. Primary processing of the images and DEM construction were carried out in the Agisoft PhotoScan programme. DEMs were constructed in two ways: with reference to the Ground Control Points (GCPs) (Fig. 4.2: Nos 1, 2, 3, 4, 5, 14) and without reference to GCPs, using the coordinates of the onboard GNSS UAV receivers. In all, 30 flights were conducted but 5 of these were not included in the analysis due to various problems and failures: operator errors in the preparation of flight routes, inaccuracy of the substrate map in the mobile application, and inadvertent camouflage (especially in summer), which interfered with orientation on the ground. Another was the absence of a reserve zone in the route of the target area, which should be laid beyond the extreme control points. Where identification of control points was impossible or optimal image overlap was not achieved, the data for that flight was

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Fig. 4.2 Topographic map of the landfill showing Ground Control Points 1–14

Table 4.1 Brief description of UAVs and cameras UAV

Weight, g

Max. speed m/sec

Camera type

Camera resolution, Mp

DJI Mavic Air

430

18

FC220

12

DJI Inspire-1

2845

10

X3 FC350

12

X5 FC550

16

DJI Phantom 2 Vision+

1242

15

FC200

14

DJI Phantom 3 Advanced

1280

16

FC300C

13

DJI Phantom 4 Pro

1388

20

FC6310

20

Custom UAV Lady Bug

3000

10

Canon Power Shot S100

12

Hawkeye Firefly 6C 12

not used so the total number of DEMs analysed was: 19 without reference to GCPs, and 15 with reference to GCPs. Analysis and assessment of the accuracy of the DEMs was carried out in ArcGIS software where orthophotomaps and DEMs were constructed with the help of Agisoft PhotoScan. An orthophotomap is necessary for visualising the terrain, while the DEM makes it possible to determine the values of each pixel by height. The average absolute errors, average height errors, root mean square errors (RMSE), plan RMSE (XY) and

4 Creating Digital Elevation Models Using Budget …

41

Table 4.2 Accuracy indicators of the obtained models (option 1) UAV

Camera

DJI Mavic Air

FC220

DJI Inspire-1 DJI Inspire-1

Shooting height

Number of shots

RMSE(XY), m

RMSE (H/Z), m

50

251

4.712

2.652

X3 FC350

25

169

2.344

0.143

X3 FC350

50

217

2.134

3.482

DJI Inspire-1

X3 FC350

100

40

1.854

0.282

DJI Inspire-1

X5 FC550

50

193

1.416

0.853

DJI Inspire-1

X5 FC550

100

64

2.109

0.435

DJI Inspire-1

X5 FC550

100

44

1.869

0.476

DJI Inspire-11

X5 FC550

100

22

4.285

0.588

DJI Phantom 2 Vision+

FC200

50

347

2.711

0.265

DJI Phantom 2 Vision+

FC200

50

77

3.473

1.818

DJI Phantom 3 Advanced

FC300C

25

482

0.382

0.455

DJI Phantom 3 Advanced

FC300C

25

285

0.694

1.405

DJI Phantom 3 Advanced

FC300C

50

260

0.439

0.240

DJI Phantom 4 Pro

FC6310

50

256

1.799

0.658

DJI Phantom 4 Pro

FC6310

100

146

1.784

0.787

Custom UAV Lady Bug

Canon Power shot S100

25

593

1.697

4.856

Custom UAV Lady Bug

Canon Power shot S100

25

295

1.979

3.805

Custom UAV Lady Bug

Canon Power shot S100

50

528

0.506

4.916

Custom UAV Lady Bug

Canon power shot S100

50

264

0.513

4.936

RMSE height (Z/H) were calculated. The minimum and maximum values of absolute and altitude errors were determined. In further analysis the main indicators of the models’ quality were RMSEs.

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Results DEM accuracy results obtained without using reference points are presented in Table 4.2. Most of the UAVs we used belong to the middle-price bracket and are not intended for accurate scientific research. Accordingly, the question of the reproducibility of the results arises. Their accuracy may be ascertained only by repeated flights under similar conditions, as we have done. However, with the exception of MartínezCarricondo and others (2018), there is no mention of such experiments in the literature (Sanz-Ablanedo and others 2018; Tilly and others 2016; Mancini and others 2013; Agüera-Vega and others 2018). In most cases it is assumed, by default, that the accuracy of the studies is constant (Pichon and others 2016). Table 4.3 presents the results of the DEM accuracy assessment, obtained using 6 reference points. Table 4.3 Accuracy indicators of models obtained using reference points (option 2) UAV

Camera

Shooting height

Number of shots

RMSE (XY), m

RMSE (Z/H) m

DJI Mavic Air

FC220

50

168

7.144

4.682

DJI Inspire-1

X3 FC350

25

169

2.383

3.98

DJI Inspire-1

X3 FC350

50

217

1.851

2.648

DJI Inspire-1

X3 FC350

100

40

2.280

2.574

DJI Inspire-1

X5 FC550

50

193

1.300

6.352

DJI Inspire-1

X5 FC550

100

83

2.118

2.343

DJI Phantom 2 Vision+

FC200

50

181

0.315

0.284

DJI Phantom 2 Vision+

FC200

50

77

0.416

4.165

DJI Phantom 3 Advanced

FC300C

25

482

0.198

2.061

DJI Phantom 4 Pro

FC6310

50

256

4.641

1.710

DJI Phantom 4 Pro

FC6310

100

146

1.823

2.093

Custom UAV Lady Bug

Firefly 6C

25

336

1.330

3.349

Custom UAV Lady Bug

Firefly 6C

50

320

0.165

2.241

Custom UAV Lady Bug

Canon Power Shot S100

25

557

0.469

2.947

Custom UAV Lady Bug

Canon Power Shot S100

50

278

0.184

2.199

4 Creating Digital Elevation Models Using Budget …

43

Discussion The initial analysis of the results (Fig. 4.3) was compromised because it was not

Fig. 4.3 Ratio of the vertical and horizontal mean square errors (Root Mean Square Error) for samples at individual heights

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possible to comply with the full scheme of using all devices at the same flying heights. Nevertheless, general patterns have been traced. So, with increasing flying height, the models’ accuracy in the horizontal dimension decreases. At the same time, altitude accuracy is greatest at the maximum fling height (100 m) but this can be attributed to the heterogeneity of the sample—at this flying height, only the Inspire-1 and Phantom 4 Pro UAVs were involved. For a flying height of 25 m, the DJI Phantom 3 Advanced UAV showed the best model accuracy. In conditions without reference to ground points (option 1) the average RMSE (Z/H) value over two repetitions was 0.538 m, RMSE (XY) was 0.93 m. Using ground control points to attach an image (option 2) significantly improved RMSE (Z/H) to 0.198 m, but RMSE (XY) significantly deteriorated to 2.061 m. For a flying height of 50 m (option 1), DJI Phantom 3 Advanced was also the most accurate: RMSE (Z/H) was 0.439 m, RMSE (XY) was 0.240 m. It is interesting that other models in this series (DJI Phantom 2 Vision + and DJI Phantom 4 Pro) showed significantly worse results. With the images linked to ground points, the Lady Bug UAV had the best RMSE (Z/H) values: 0.165 m with the Firefly 6C camera and 0.184 m with the Canon Power Shot S100. It also showed good RMSE (XY) values: 2.241 m with the Firefly 6C camera and 2.199 m with the Canon Power Shot S100 camera. Second place went to the DJI Phantom 2 Vision + with an average RMSE (Z/H) value for two repetitions of 0.365, and RMSE (XY) 2.224 m. Unfortunately, for technical reasons, it was not possible to obtain a model from the results of a survey conducted by DJI Phantom 3 Advanced. Nor could all of the above devices take part in shots from a height of 100 m. For option 1, DJI Inspire-1 with an X3 FC350 camera showed the best accuracy: RMSE (Z/H) = 1.854 m, RMSE (XY) = 0.282 m. It should be noted that results deteriorated when using more powerful X5 FC550 camera on the same device. Note the variability and instability of the results. Analysis of each UAV’s results at different altitudes (Fig. 4.4) shows that, in some cases, there is a greater accuracy of the model with a higher altitude of the UAV; counter-intuitively, for DJI Inspire-1 (X3 FC350 camera), the best plan accuracy of the model was obtained during shooting from a height of 100 m. Similarly, for Custom Lady Bug (Canon Power Shot S100 camera), the results of shooting at a height of 50 m were better than from a height of 25 m; flights by Custom Lady Bug at both heights were performed in duplicate. In general, a small number of repetitions does not allow definitive conclusions about the accuracy of UAVs as geodetic instruments. In our research, UAV Custom Lady Bug turned out to be the most stable in terms of repetition. Variations of its RMSE (Z/H) and RMSE (XY) are insignificant (Table 4.2). The largest number of repetitions was performed for the DJI Inspire-1 (X5FC550 camera). The three flights at an altitude of 100 m show significant variations in RMSE (Z/H) (from 1.869 m to 4.285 m) but fairly small variations in RMSE (XY) (from 0.435 m to 0.588 m). Significant excess of errors in the horizontal dimension compared with the vertical dimension is surprising but might be explained by an insufficient factual base and possible errors in the construction of the DEMs.

4 Creating Digital Elevation Models Using Budget …

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25m

50m

100m

Without reference to GCPs With reference to GCPs

Fig. 4.4 Ratio of the vertical and horizontal mean square errors for the samples for individual cameras

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An interesting fact is that the use of ground control points does not always improve the quality of the model (Fig. 4.4). A possible explanation for this is the insufficient number of anchor points.

Conclusions Our evaluation of the accuracy of DEMs obtained from survey data acquired by budget UAV options showed that they can be used to create large-scale topographic and thematic maps, even taking into account their discrepancies. In particular, the results of surveying with Phantom 3 Advanced at heights of 25 and 50 m correspond to a scale accuracy of 1: 5000 topographic maps (Topographic Survey of Ukraine 1999). As for the construction of soil maps, we can safely recommend any of the UAVs used to build maps of scale 1: 5000. Bearing in mind that the UAVs tested belong to the middle-price bracket, we may envisage their widespread use in agriculture. At the same time, the significant variability of RMSE in the vertical and horizontal dimensions should be noted. Unfortunately, it was not possible to quantitatively establish the extent to which the accuracy indicators of models are improved by reference to ground points, so it remains an open question whether the budget UAV models should be used to construct more-detailed topographic maps.

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Tilly N, D Kelterbaum and R Zeese 2016 Geomorphological mapping with terrestrial laser scanning and uav-based imaging. International Archives of the Photogrammetry, Remote Sensing and. Spatial Information Sciences XLI-B5, 591–597, https://doi.org/10.5194/isprs-archives-xli-b5591-2016. Topographic Survey of Ukraine 1999 Topographic Survey Instruction for Scales 1: 5000, 1: 2000, 1: 1000 and 1: 500 (GKNTA-2.04–02-98) z0393-98. http://zakon5.rada.gov.ua/laws/show/z03 93-98 (Ukrainian). van der Wal, T., B. Abma, A. Viguria, et al. 2013. Fieldcopter: unmanned aerial systems for crop monitoring services. Precision Agriculture 13: 169–175. Zhang, C., and J.M. Kovacs. 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13 (6): 693–712. https://doi.org/10.1007/s11119-0129274-5.

Chapter 5

Determination of Eroded Chernozem on the Right-Bank Steppe of Ukraine Using the Soil Line Sergiy Chornyy , Dmitriy Abramov, and Daria Sadova

Abstract Soil lines have been created using data from multi-spectral imagery to identify and map the soil cover of the Right-Bank Steppe of Ukraine. Images taken by the Operational Land Imager mounted on Landsat-8 were analysed using the QGIS geographical information system along with graphical and statistical analysis. Brightness in the red (RED) and near-infrared (NIR) parts of the spectrum was measured for bare soil landscapes and compared with field data from research sites on non-eroded and eroded Ordinary and Southern chernozem. On the basis of these data, soil lines were constructed. Statistical analysis of the samples and parameters of the equation NIR = f (RED) shows the uniqueness of the spectral characteristics of various sub-types of chernozem; the parameters of the soil line (β 1 , β 0 ) and the magnitude of the coefficient of determination (r 2 ) are statistically significant for specific soil characteristics, in particular the content of humus, carbonates, and (for Southern chernozem) sodicity. Soil lines built in hyperspectral space can be used to identify and map various sub-types of chernozem and their eroded variants. Keywords Multi-spectral imagery · Soil line · Spectral brightness · Ordinary chernozem · Southern chernozem

Introduction The problem of mapping the soil cover of Ukraine is well known; in the case of largescale maps (scales 1:5,000, 1:10,000, and 1:25,000) needed for land use planning and management, it is urgent. Satellite imagery of large areas offers an opportunity to perform such mapping quickly and efficiently. One of the most successful concepts is the soil line, the linear relationship between the values of red (RED) and near-infrared (NIR) spectra in hyperspectral space obtained by a multi-spectral scan of the bare soil surface (Baret and others 1993; Fox and others 2004; Galvao and Vitorello 1998; Kauth and Thomas 1976; Yoshioka and others 2009). Such a line is described by the ordinary linear equation: S. Chornyy (B) · D. Abramov · D. Sadova Mykolayiv National Agrarian University, 9, Georgiya Gongadze St, Mykolayiv 54020, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Dmytruk and D. Dent (eds.), Soils Under Stress, https://doi.org/10.1007/978-3-030-68394-8_5

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N I R = R E D × β0 + β1

(1)

where NIR is the value of brightness in the near-infrared part of the spectrum, RED is the value of brightness in the red part of the spectrum, β0 is the tangent of the angle of inclination, and β1 is the distance along the y-axis from the point of intersection to the beginning of the axis.

Fox and others (2004), investigating soils in Texas and the American Mid-west, asserted that there are no unique soil lines for individual soil types and sub-types. However, several other researchers (Demattê and others 2009; Kiryanova and Savin 2011; Chornyy et al. 2013) have found that, most likely, each soil type and sub-type corresponds to its own soil line with specific values for parameters β 1 and β 0 . In particular, Demattê and Marcos (2006) working in São Paulo, Brazil, have developed a technique for using multi-spectral remote sensing to construct soil lines for different soils—characterizing them according to the content of clay < 0.01 mm, properties of the parent rock, and the iron content. Recent work on soils of the Tula region of Russia (Rukhovich and others 2016) confirms the prospect of using the soil line for their identification and mapping.

Materials and Methods The object of study was the soil lines of Ordinary chernozem and Southern chernozem, together with their eroded sloping facies, at four sites located within the Right-Bank Steppe of Ukraine (Table 5.1). Erosion has a severe impact on the structure of the soil cover in this region and is a main cause of its complexity; in the Mykolayiv region, alone, 815 thousand ha (one-third of the total area) is eroded. Satellite images obtained by the Operational Land Imager aboard the Landsat-8 satellite have a spatial resolution of 30 m for eight spectral channels. Two of these are used to construct the soil lines: the red (RED, 0.64–0.67 μm) and near-infrared (NIR, Table 5.1 Characteristics of the study sites № №

Soils

Coordinates of sites Latitude(N)

Longitude (E)

Dates of the images used in the calculations

Number of selected pixels Non-eroded soils

Eroded soils

1.

Southern chernozem

46.905448

31.679024

04.04.17, 31.03. 18

94

140

2.

Southern chernozem

46.892311

31.682028

04.04.17, 31.03.18

84

110

3.

Southern chernozem

47.353425

32.874279

28.03.17, 13.04.17

148

314

4.

Ordinary chernozem

47.826719

31.318726

29.10.17, 08.10.15

386

488

5 Determination of Eroded Chernozem on the Right-Bank …

51

0.85–0.88 μm). QGIS 2.18 open-source software was used to determine the spectral brightness of the studied landscapes as a unitless variable from 0 to 1. Before directly determining the spectral brightness on the metadata obtained with satellite images, pre-processing of the images included atmospheric and radiometric correction of the data using the Semi-Automatic Classification Plug-in for QGIS 2.18. Using geographic data, satellite images were combined in QGIS 2.18 with data from pre-field studies to identify soil erosion and determine the basic properties of soils in the study areas (Table 5.2). In addition to field research, archival materials of Zemproekt-Mikolayiev LLC were also used to identify the soils of the study sites. As soil lines may be determined only in the absence of vegetation, the surfaces of the test plots were verified for all available cloudless images of the Landsat-8 OLI scanner for 2015–2018. The presence or absence of vegetation was determined using the Normalized Difference Vegetation Index (NDVI): N DV I =

N I R − RE D N I R + RE D

(2)

If NDVI is in the range 0–0.32, the soil surface is considered to be bare or the vegetation too sparse to have any effect on the reflective properties of the soil. On this basis, six cloudless satellite images from 08.10.2015, 28.03.2017, 04.04.2017, 13.04.2017, 29.10.2017, and 31.03.2018 were selected (Table 5.1). Soil lines were constructed using MS Excel 2010; the data analysis package of the same software was used for statistical estimation of the closeness and significance of the obtained regression equations NIR = f(RED) and parameters β 1 , β 0 . In addition to measuring the parameters of the soil line, a statistical analysis of the brightness of the red and infrared spectra of these satellite images was performed using the Fisher, Student, and Pearson tests. Table 5.2 Some properties of the topsoil of the study (numerator–watershed, denominator–slope) Parameters

Soils Southern chernozem

Southern chernozem

Ordinary chernozem

Latitude (N)

47.353425

46.892311

47.826719

46.905448

Longitude (E)

32.874279

31.682028

31.318726

31.679024

Humus, %

2.44/2.28

3.30/3.20

4.31/4.03

3.21/2.86

Power of a soil horizon A + AB, cm

52/49

52/45

56/50

55/44

Coordinates of sites

Southern chernozem

Carbonates, %

3.33/7.28

0.20/0.13

0.28/0.62

0.20/0.42

Particle size, % 10 mm and < 0.25 mm to the sum of fractions 10–0.25 mm indicates the optimal aggregate-size distribution; the greater the ratio, the better aggregate-size distribution and we find that the ratio has increased by 1.5 under the black locust plantation and by 2.8 under the oak plantation. Whether this difference is a species effect or an effect of duration of the woodland, we cannot tell.

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In the A horizon, the change in the size distribution of water-stable of aggregates compared with arable land is significant in size fractions from > 5 mm down to 1– 0.5 mm. This is most marked in the size fractions of 3–2 and 2–1 mm under the oak plantation, which is accompanied by a reduction in bulk density and an increase in total pore space. This represents a significant improvement in soil physical properties in a relatively short period under woodland; the results are consistent with those reported by Li and others (2012). Over the longer period of 150 years, Li and Shao (2006) note that woodland plantations leads to a decrease in soil density values and an increase of total porosity. We should expect this improvement to be reflected in a greater infiltration rate, better available water capacity and increased resilience against erosion. Already, the A horizon itself is thicker by 10–20 cm under the woodland plantations compared with the adjacent arable land; which brings us full circle.

Conclusions • R. pseudoacacia and Q. robur plantations have a significant, measurable, beneficial effect on the complex of physical properties of Calcic chernozem. This is manifest most strongly in the A horizon. • Compared with the sampled profile from arable land, the A horizons of the two profiles under forest plantations show a 4–6% higher content of sand and silt and a 5–8% lower content of clay, which may or may not be connected with the woodland plantations. • We can be confident that the woodland plantations are responsible for an increase in the content of soil aggregates in the 1–2 mm and 2–3 mm size fractions, and a significant increase in the proportion of water-stable aggregates larger than 0.5 mm, especially those greater than 1 mm. The 70-year-old oak plantation has had a greater effect than the 50-year-old black locust plantation. • Changes in bulk density, particle density and total porosity follow the same pattern: the 70-year-old oak plantation producing lower bulk density and particle density and greater total pore space in the A horizon, and these effects extend more strongly into the underlying Bk horizon, than under the 50-year-old black locust plantation. Acknowledgements The presented work is part of the scientific research of the Dept of Geobotany, Soil Science and Ecology of the Oles Honchar Dnipro National University, which is devoted to establishing the influences of forest plantations on steppe soils.

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References Banning, N.C., C.D. Grant, D.L. Jones, and D.V. Murphy. 2008. Recovery of soil organic matter, organic matter turnover and nitrogen cycling in a post-mining forest rehabilitation chronosequence. Soil Biology and Biochemistry 40 (8): 2021–2031. https://doi.org/10.1016/j.soilbio.2008. 04.010. Carter, M.R., and E.G. Gregorich, eds. 2006. Soil sampling and methods of analysis, 2nd ed. Boca Raton FL: CRC Press. https://doi.org/10.1201/9781420005271. Clark, J.D., and A.H. Johnson. 2011. Carbon and nitrogen accumulation in post-agricultural forest soils of western New England. Soil Science Society of America Journal 75 (4): 1530–1542. https:// doi.org/10.2136/sssaj2010.0180. Dent, D.L. 2019. A standard for soil health. International Journal of Environmental Studies 77 (4): 613–618. https://doi.org/10.1080/00207233.2019.169020. FAO. 2006. Guidelines for soil description, 4th ed. Rome: Food and Agriculture Organization of the United Nations. Foote, R.L., and P. Grogan. 2010. Soil carbon accumulation during temperate forest succession on abandoned low productivity agricultural lands. Ecosystems 13 (6): 795–812. https://doi.org/10. 1007/s10021-010-9355-0. Gu, C., X. Mu, P. Gao, et al. 2019. Influence of vegetation restoration on soil physical properties in the Loess Plateau. China. Journal of Soils and Sediments 19 (2): 716–728. https://doi.org/10. 1007/s11368-018-2083-3. IUSS Working Group WRB. 2015. World Reference Base for Soil Resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Report 106. Rome: Food and Agriculture Organization of the United Nations. Jiang, C., J. Liu, H. Zhang, et al. 2019. China’s progress towards sustainable land degradation control: Insights from the northwest arid regions. Ecological Engineering 127: 75–87. https:// doi.org/10.1016/j.ecoleng.2018.11.014. Jiao, F., Z.M. Wen, and S.S. An. 2011. Changes in soil properties across a chronosequence of vegetation restoration on the Loess Plateau of China. CATENA 86 (2): 110–116. https://doi.org/ 10.1016/j.catena.2011.03.001. Lal, R. 2005. Forest soils and carbon sequestration. Forest Ecology and Management 220 (1–3): 242–258. https://doi.org/10.1016/j.foreco.2005.08.015. Li, W., M. Yan, Z. Qingfeng, and J. Zhikaun. 2012. Effects of vegetation restoration on soil physical properties in the wind-water erosion region of the Northern Loess Plateau of China. Clean - Soil, Air, Water 40 (1): 7–15. https://doi.org/10.1002/clen.201100367. Li, Y.Y., and M.A. Shao. 2006. Change of soil physical properties under long-term natural vegetation restoration in the Loess Plateau of China. Journal of Arid Environments 64 (1): 77–96. https:// doi.org/10.1016/j.jaridenv.2005.04.005. Medvedev, V.V., I.V. Plisko, and O.N. Bigun. 2014. Comparative characterization of the optimum and actual parameters of Ukrainian chernozems. Eurasian Soil Science 47 (10): 1044–1057. https://doi.org/10.1134/S106422931410007X. Shangguan, W., Y. Dai, B. Liu, et al. 2012. A soil particle-size distribution dataset for regional land and climate modelling in China. Geoderma 171–172: 85–91. https://doi.org/10.1016/j.geoderma. 2011.01.013. Wang, B., X. Zhao, Y. Liu, et al. 2019. Using soil aggregate stability and erodibility to evaluate the sustainability of large-scale afforestation of Robinia pseudoacacia and Caragana korshinskii in the Loess Plateau. Forest Ecology and Management 450: 117491. https://doi.org/10.1016/j.for eco.2019.117491. Wi´sniewski, P., and M. Märker. 2019. The role of soil-protecting forests in reducing soil erosion in young glacial landscapes of Northern-Central Poland. Geoderma 337: 1227–1235. https://doi. org/10.1016/j.geoderma.2018.11.035.

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Wunder, S., and R. Bodle. 2019. Achieving land degradation neutrality in Germany: Implementation process and design of a land use change based indicator. Environmental Science and Policy 92: 46–55. https://doi.org/10.1016/j.envsci.2018.09.022. Zhang, X., J.F. Adamowski, R.C. Deo and others. 2018. Effects of afforestation on soil bulk density and pH in the Loess Plateau, China. Water 10 (12): 1710. Switzerland. https://doi.org/10.3390/ w10121710. Zhang, X., Z. Yang, T. Zha, et al. 2017. Changes in the physical properties of soil in forestlands after 22 years under the influence of the conversion of cropland into farmland project in Loess region, Western Shanxi Province. Shengtai Xuebao/Acta Ecologica Sinica 37 (2): 416–424. https://doi. org/10.5846/stxb201507291596.

Chapter 11

Transformation of Physical Indicators of Soil Fertility in Typical Chernozem of the Eastern Forest-Steppe of Ukraine Yurii Dehtiar’ov , Dmytro Havva , Natalia Kovalzhy , and Sergiy Rieznik Abstract Determination of the physical state (particle-size and aggregate-size distribution, soil structure, consistency, bulk density and porosity) and electrical conductivity of thick Typical chernozem reveals its complex genesis, the formation of the soil profile, and indicators of soil fertility. Humus-accumulative soil formation is active under woodland (oak, birch, larch, pine) as well as under perennial grassland. Restorative use of Chernozem soils contributes to the renewal of the whole set of physical indicators of soil fertility. The same indicators reveal the opposite direction of travel under arable. Keywords Chernozem · Physical characteristics · Woodland · Natural grassland · Agrogenic soils

Introduction Because they are the best arable soils in the world, natural Chernozem are preserved only in nature reserves and a few other sites that remain uncultivated. All the rest are much changed by cultivation. Analysis of physical parameters of thick Typical chernozem of the eastern forest-steppe of Ukraine (particle-size and aggregate-size distribution, bulk density and particle density, porosity and electrical conductivity) should reveal their complex origin (Tykhonenko 2001; Tykhonenko and Dehtyar’ov 2014), the processes of formation of their profile, and indicators of soil fertility.

Y. Dehtiar’ov (B) · D. Havva · N. Kovalzhy · S. Rieznik Kharkiv National Agrarian University named after V.V. Dokuchayev, Dokuchaevske-2, Kharkiv 62483, Ukraine e-mail: [email protected] D. Havva e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. Dmytruk and D. Dent (eds.), Soils Under Stress, https://doi.org/10.1007/978-3-030-68394-8_11

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Materials and Methods Our field site was the Research Field of the Study, Research and Production Centre (SRPC) in Kharkiv District that was laid out in 1946 under Professor OM Grinchenko. The soil is thick Typical chernozem that has been tilled for more than a century. Since 1946, some has been set aside as natural grassland (Sect. 1), most is still cultivated under arable rotations (Sect. 2), some is under a shelterbelt of oak woodland. Soil pits were also examined in Typical chernozem that was being tilled until 1972 and subsequently, after the establishment of the university arboretum, evolved under the canopy of (4) larch, (5) birch, (6) pine with grassy ground vegetation, and (7) mown grass. At the selected sites, we described the soil profile and the nature of the soil surface. Soil samples were collected from each genetic horizon. Average samples were prepared by mixing several individual samples from the same depth. Samples were analysed as described in Petrenko and others (2013): particle-size distribution by Kachinsky’s modification of the pipette method (DSTU B V. 2.1-19:2009); particle density by pycnometer (DSTU 4745: 2007); bulk density and, hence, calculated total porosity; micro-aggregate composition by MMV 31-497,058-011-2005 and, thereby, calculated the coefficient of dispersion and structure; structural and aggregate composition after Savinov (DSTU 4744:2007); and electrical conductivity of a 1:5 soil:water suspension using an EZODO-8200 instrument (DSTU 8346:2015). Tests were carried out in five repetitions and statistical analysis of the data made use of the systems of Excel spreadsheets.

Results and Discussion Particle-Size and Aggregate-Size Distribution Particle-size distribution is an important factor in many soil physical and chemical processes, and it gives an indication of the mineralogy and thus nutrient status (Medvedev 2013). Thick Typical chernozem are heavy loams, characterized by 53– 65% physical clay (equivalent diameter