Digital technology dilemma: on unlocking the soil quality index conundrum

Abstract

Knowledge of the interactions between soil systems, management practices, and climatic extremes are critical for prescription-based sustainable practices that reduce environmental pollution/footprints, disruption of food supply chains, food contamination, and thus improve socio-economic wellbeing. Soil quality status and dynamics under climate change present both a hazard which may not be remedied by simply adding chemicals or improved by crop varieties, and an opportunity (e.g., by indicating impact of a shift in land use) although the specifics remain debatable. This entry not only revisits the science of soil quality determination but also explicates on intricacies of monitoring using big data generated continuously and integrated using the “internet of things.” Indeed, relaying credible soil quality information especially for heterogeneous soils at field scale is constrained by challenges ranging from data artifacts and acquisition timing differences, vague baselines, validation challenges, scarcity of robust standard algorithms, and decision support tools. With the advent of digital technology, modern communication networks, and advancement in variable rate technologies (VRT), a new era has dawned for developing automated scalable and synthesized soil quality metrics. However, before digital technology becomes the routine tool for soil quality sensing and monitoring, there is need to understand the issues and concerns. This contribution not only exemplifies a unique application of digital technology to detect residue cover but also deliberates on the following questions: (1) is digital agriculture the missing link for integrating, understanding the interconnectivity, and ascertaining the provenance between soil quality, agronomic production, environmental health, and climate dynamics? and (2) what are the technological gaps?

Keywords

Accuracy / Digital mapping / Soil quality / Spatial interpolation

References

[1] Abbas A, Khan S, Hussain N, Hanjra MA, Akbar S. Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Phys Chem Earth, 2013, 55–57: 43-52.

[2] Andrews SS, Carroll CR. Designing a soil quality assessment tool for sustainable agroecosystem management. Ecol Appl, 2001, 11(6): 1573-1585.

[3] Andrews SS, Flora CB, Mitchell JP, Karlen DL. Growers’ perceptions and acceptance of soil quality indices. Geoderma, 2003, 114(3–4): 187-213.

[4] Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, Van Griensven A, Van Liew MW, Kannan N. SWAT: model use, calibration, and validation. Trans ASABE, 2012, 55(4): 1491-1508.

[5] Arshad MA, Martin S. Identifying critical limits for soil quality indicators in agro-ecosystems. Agr Ecosyst Environ, 2002, 88(2): 153-160.

[6] Bai Z, Caspari T, Gonzalez MR, Batjes NH, Mäder P, Bünemann EK, de Goede R, Brussaard L, Xu M, Ferreira CSS, Reintam E, Fan H, Mihelič R, Glavan M, Tóth Z. Effects of agricultural management practices on soil quality: a review of long-term experiments for Europe and China. Agr Ecosyst Environ, 2018, 265: 1-7.

[7] Batjes NH. Soil organic carbon stocks under native vegetation–revised estimates for use with the simple assessment option of the Carbon Benefits Project system. Agr Ecosyst Environ, 2011, 142(3): 365-373.

[8] Bentley JW, Van Mele P, Barres NF, Okry F, Wanvoeke J. Smallholders download and share videos from the Internet to learn about sustainable agriculture. Int J Agric Sustain, 2019, 17(1): 92-107.

[9] Bilgili AV. Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques. Environ Monit Assess, 2013, 185(1): 777-795.

[10] Bouma J, McBratney A. Framing soils as an actor when dealing with wicked environmental problems. Geoderma, 2013, 200–201: 130-139.

[11] Breiman L. Random forests. Mach Learn, 2001, 45(1): 5-32.

[12] Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees, 1984, Boca Raton: Chapman and Hall.

[13] Broders KD, Wallhead MW, Austin GD, Lipps PE, Paul PA, Mullen RW, Dorrance AE. Association of soil chemical and physical properties with Pythium species diversity, community composition, and disease incidence. Phytopathology, 2009, 99(8): 957-967.

[14] Bünemann EK, Bongiorno G, Bai Z, Creamer RE, De Deyn G, de Goede R, Fleskens L, Geissen V, Kuyper TW, Mäder P, Pulleman M, Sukkel W, van Groenigen JW, Brussaard L. Soil quality–a critical review. Soil Biol Biochem, 2018, 120: 105-125.

[15] Calhoun FG, Smeck NE, Slater BL, Bigham JM, Hall GF. Predicting bulk density of Ohio Soils from morphology, genet principles, and laboratory characterization data. Soil Sci Soc Am J, 2001, 65(3): 811-819.

[16] Chang N-B, Imen S, Vannah B. Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Crit Rev Environ Sci Technol, 2015, 45(2): 101-166.

[17] Chen SC, Liang ZZ, Webster R, Zhang GL, Zhou Y, Teng HF, Hu BF, Arrouays D, Shi Z. A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution. Sci Total Environ, 2019, 655: 273-283.

[18] Cohen M, Mylavarapu RS, Bogrekci I, Lee WS, Clark MW. Reflectance spectroscopy for routine agronomic soil analyses. Soil Sci., 2007, 172(6): 469-485.

[19] Colles FM, Cain RJ, Nickson T, Smith AL, Roberts SJ, Maiden MCJ, Lunn D, Dawkins MS. Monitoring chicken flock behaviour provides early warning of infection by human pathogen Campylobacter. Proc Royal Soc B Biol Sci, 2016, 283(1822): 20152323.

[20] Congalton RG. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ, 1991, 37(1): 35-46.

[21] Dash JP, Watt MS, Pearse GD, Heaphy M, Dungey HS. Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. Isprs J Photogramm Remote Sensing, 2017, 131: 1-14.

[22] Davis BM. Uses and abuses of cross-validation in geostatistics. Math Geol, 1987, 19(3): 241-248.

[23] de Paul Obade V (2011) Crop residue cover assessment using remotely sensed data. Plant Science Department, South Dakota State University

[24] de Paul Obade V. Integrating management information with soil quality dynamics to monitor agricultural productivity. Sci Total Environ, 2019, 651: 2036-2043.

[25] de Paul Obade V, Lal R. Assessing land cover and soil quality by remote sensing and geographical information systems (GIS). CATENA, 2013, 104: 77-92.

[26] de Paul Obade, V., Lal, R. 2014a. Soil quality evaluation under different land management practices. Environmental Earth Sciences, 1–19

[27] de Paul Obade V, Lal R. Using meta-analyses to assess pedo-variability under different land uses and soil management in central Ohio, USA. Geoderma, 2014, 232–234: 56-68.

[28] de Paul Obade V, Lal R. A standardized soil quality index for diverse field conditions. Sci Total Environ, 2016, 541: 424-434.

[29] de Paul Obade V, Lal R. Towards a standard technique for soil quality assessment. Geoderma, 2016, 265: 96-102.

[30] de Paul Obade V, Moore R. Synthesizing water quality indicators from standardized geospatial information to remedy water security challenges: a review. Environ Int, 2018, 119: 220-231.

[31] de Paul Obade V, Lal R, Chen J. Remote sensing of soil and water quality in agroecosystems. Water Air Soil Pollut, 2013, 224(9): 1-27.

[32] de Paul Obade V, Lal R, Moore R. Assessing the accuracy of soil and water quality characterization using remote sensing. Water Resour Manage, 2014, 28(14): 5091-5109.

[33] Dematte JM, Galdos RV, Guimaraes AM, Gnu MR, Zullo J. Quantificaion of tropical soil attributes from ETM +/LANDSAT-7data. Int J Remote Sensing, 2007, 28(17): 3813-3829.

[34] Diek S, Chabrillat S, Nocita M, Schaepman ME, de Jong R. Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping. Geoderma, 2019, 337: 607-621.

[35] Doran JW, Parkin TB (1994) Defining and assessing soil quality. Defining Soil Quality for a Sustainable Environment, (Eds.) J.W. Doran, D.C. Coleman, D.F. Bezdicek, B.A. Stewart, SSSA, Inc. Madison, Wisconsin, USA

[36] Dumont B, Groot JCJ, Tichit M. Review: make ruminants green again-how can sustainable intensification and agroecology converge for a better future?. Animal, 2018, 12: S210-S219.

[37] Fleming A, Jakku E, Lim-Camacho L, Taylor B, Thorburn P. Is big data for big farming or for everyone? Perceptions in the Australian grains industry. Agron Sustain Dev, 2018, 38(3): 24.

[38] Gleick PH, Palaniappan M. Peak water limits to freshwater withdrawal and use. Proc Natl Acad Sci, 2010, 107(25): 11155-11162.

[39] Goovaerts P. Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 1999, 89(1–2): 1-45.

[40] Grunwald S. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 2009, 152(3–4): 195-207.

[41] Guo LB, Gifford RM. Soil carbon stocks and land use change: a meta analysis. Glob Change Biol, 2002, 8: 345-360.

[42] Haji Gholizadeh M, Melesse AM, Reddi L. Spaceborne and airborne sensors in water quality assessment. Int J Remote Sens, 2016, 37(14): 3143-3180.

[43] Hartemink AE. Soils are back on the global agenda. Soil Use Manag, 2008, 24(4): 327-330.

[44] He C, Riggs JF, Kang Y-T. Integration of geographic information systems and a computer model to evaluate impacts of agricultural runoff on water quality 1. J Am Water Resour Assoc, 1993, 29(6): 891-900.

[45] Herrick JE, Karl JW, McCord SE, Buenemann M, Riginos C, Courtright J, Ganguli A, Angerer J, Brown J, Kimiti D, Saltzman R, Beh A, Bestelmeyer B. Two new mobile apps for rangeland inventory and monitoring by landowners and Land Managers. Rangelands, 2017, 39(2): 46-55.

[46] Heung B, Bulmer CE, Schmidt MG. Predictive soil parent material mapping at a regional-scale: a random forest approach. Geoderma, 2014, 214–215: 141-154.

[47] Huang Y, Chen Z-X, Yu T, Huang X-Z, Gu X-F. Agricultural remote sensing big data: management and applications. J Integr Agric, 2018, 17(9): 1915-1931.

[48] Kamilaris A, Prenafeta-Boldú F. Deep learning in agriculture: a survey. Comput Electr Agric, 2018, 147: 70-90.

[49] Kamilaris A, Kartakoullis A, Prenafeta-Boldú F. A review on the practice of big data analysis in agriculture. Comput Electr Agric, 2017, 143: 23-37.

[50] Keskin H, Grunwald S. Regression kriging as a workhorse in the digital soil mapper’s toolbox. Geoderma, 2018, 326: 22-41.

[51] Keskin H, Grunwald S, Harris WG. Digital mapping of soil carbon fractions with machine learning. Geoderma, 2019, 339: 40-58.

[52] Ketterings QM, Bigham JM. Soil color as an indicator of slash-and-burn fire severity and soil fertility in Sumatra, Indonesia. Soil Sci Soc Am J, 2000, 64(5): 1826-1833.

[53] Khanal S, Fulton J, Shearer S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput Electron Agric, 2017, 139: 22-32.

[54] Khanal S, Fulton J, Klopfenstein A, Douridas N, Shearer S. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput Electron Agric, 2018, 153: 213-225.

[55] Khanal S, Kushal KC, Fulton JP, Shearer S, Ozkan E. Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sens, 2020, 12(22): 3783.

[56] Klaina H, Alejos AV, Aghzout O, Falcone F. Narrowband characterization of near-ground radio channel for wireless sensors networks at 5G-IoT bands. Sensors, 2018, 18(8): 2428.

[57] Kyratzis A, Skarlatos D, Fotopoulos V, Vamvakousis V, Katsiotis A. Investigating correlation among NDVI Index derived by unmanned aerial vehicle photography and grain yield under late drought stress conditions. Agric Clim Change., 2015, 29: 225-226.

[58] Lal R. Technology Without Wisdom, 2009, Dordrecht: Springer

[59] Lal R. Tragedy of the global commons: soil, water and air, 2009, Dordrecht: Springer.

[60] Lal R. Climate-strategic agriculture and the water-soil-waste nexus. J Plant Nutr Soil Sci, 2013, 176(4): 479-493.

[61] Lal R. Digging deeper: a holistic perspective of factors affecting soil organic carbon sequestration in agroecosystems. Glob Change Biol, 2018, 24(8): 3285-3301.

[62] Lal R (2020) Home gardening and urban agriculture for advancing food and nutritional security in response to the COVID-19 pandemic. Food security, 1–6

[63] Lal R, Brevik EC, Dawson L, Field D, Glaser B, Hartemink AE, Hatano R, Lascelles B, Monger C, Scholten T, Singh BR, Spiegel H, Terribile F, Basile A, Zhang Y, Horn R, Kosaki T, Sánchez LBR. Managing Soils for recovering from the COVID-19 Pandemic. Soil Syst, 2020, 4(3): 46.

[64] Landrigan PJ, Fuller R, Acosta NJR, Adeyi O, Arnold R, Basu N, Baldé AB, Bertollini R, Bose-O’Reilly S, Boufford JI, Breysse PN, Chiles T, Mahidol C, Coll-Seck AM, Cropper ML, Fobil J, Fuster V, Greenstone M, Haines A, Hanrahan D, Hunter D, Khare M, Krupnick A, Lanphear B, Lohani B, Martin K, Mathiasen KV, McTeer MA, Murray CJL, Ndahimananjara JD, Perera F, Potočnik J, Preker AS, Ramesh J, Rockström J, Salinas C, Samson LD, Sandilya K, Sly PD, Smith KR, Steiner A, Stewart RB, Suk WA, van Schayck OCP, Yadama GN, Yumkella K, Zhong M. The Lancet Commission on pollution and health. Lancet, 2018, 391(10119): 462-512.

[65] Laurent F, Ruelland D. Assessing impacts of alternative land use and agricultural practices on nitrate pollution at the catchment scale. J Hydrol, 2011, 409(1–2): 440-450.

[66] Li Q, Liu ZH, Xiao JS, Ieee. 2018. A Data Collection Collar for Vital Signs of Cows on the Grassland Based on LoRa

[67] Liou S-M, Lo S-L, Wang S-H. A generalized water quality index for Taiwan. Environ Monit Assess, 2004, 96(1): 35-52.

[68] Liu X, Guo Y, Wang QL, Zhang J, Shi Z (2013) Assessment and mapping of soil nitrogen using Visible-Near-Infrared (Vis-NIR) spectra. In: International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, (Eds.) L. Zhang, J. Yang, Vol. 8910

[69] Manandhar R, Odeh IOA. Interrelationships of land use/cover change and topography with soil acidity and salinity as indicators of land degradation. Land, 2014, 3(1): 282.

[70] Mattikalli NM, Richards K. Estimation of surface water quality changes in response to land use change: application of the export coefficient model using remote sensing and geographical information system. J Environ Manage, 1996, 48(3): 263-282.

[71] Mc Inerney B, Corkery G, Ayalew G, Ward S, Mc Donnell K. Preliminary in vivo study on the potential application of a novel method of e-tracking to facilitate traceability in the poultry food chain. Comput Electron Agric, 2011, 77(1): 1-6.

[72] McBratney AB, Minasny B, Cattle SR, Vervoort RW. From pedotransfer functions to soil inference systems. Geoderma, 2002, 109(1): 41-73.

[73] McBratney AB, Minasny B, Tranter G. Necessary meta-data for pedotransfer functions. Geoderma, 2011, 160(3): 627-629.

[74] McBratney A, Field DJ, Koch A. The dimensions of soil security. Geoderma, 2014, 213: 203-213.

[75] Mehmood T, Liland KH, Snipen L, Saebo S. A review of variable selection methods in Partial Least Squares Regression. Chemometr Intell Lab Syst, 2012, 118: 62-69.

[76] Minasny B, Hartemink AE. Predicting soil properties in the tropics. Earth Sci Rev, 2011, 106(1–2): 52-62.

[77] Nabiollahi K, Golmohamadi F, Taghizadeh-Mehrjardi R, Kerry R, Davari M. Assessing the effects of slope gradient and land use change on soil quality degradation through digital mapping of soil quality indices and soil loss rate. Geoderma, 2018, 318: 16-28.

[78] Nakarmi AD, Tang L, Xin H. Automated tracking and behavior quantification of laying hens using 3D computer vision and radio frequency identification technologies. Trans Asabe, 2014, 57(5): 1455-1472.

[79] Ndehedehe CE, Anyah RO, Alsdorf D, Agutu NO, Ferreira VG. Modelling the impacts of global multi-scale climatic drivers on hydro-climatic extremes (1901–2014) over the Congo basin. Sci Total Environ, 2019, 651: 1569-1587.

[80] Nelson DW, Sommers LE (1996). Total carbon, organic carbon, and organic matter. 2nd ed. in: Am. Soc. of Agron., (Eds.) Sparks DL, et al. Vol. 9, SSA. Madison, WI, p. 961–1010

[81] Ngo-Mbogba M, Yemefack M, Nyeck B. Assessing soil quality under different land cover types within shifting agriculture in South Cameroon. Soil Tillage Res, 2015, 150: 124-131.

[82] NOAA. 2020. NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters Vol. 2020. https://www.ncdc.noaa.gov/billions/

[83] Nocita M, Stevens A, Noon C, van Wesemael B. Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma, 2013, 199: 37-42.

[84] Nortcliff S. Standardisation of soil quality attributes. Agr Ecosyst Environ, 2002, 88(2): 161-168.

[85] Odeh IOA, McBratney AB, Chittleborough DJ. Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma, 1995, 67(3–4): 215-226.

[86] Ohlson K. The soil will save us: How scientists, farmers, and foodies are healing the soil to save the planet, 2014, Emmaus: Rodale.

[87] Ouma YO. Advancements in medium and high resolution Earth observation for land-surface imaging: evolutions, future trends and contributions to sustainable development. Adv Space Res, 2016, 57(1): 110-126.

[88] Paz-Ferreiro J, Fu S. Biological indices for soil quality evaluation: perspectives and limitations. Land Degrad Dev, 2016, 27(1): 14-25.

[89] Pigford AAE, Hickey GM, Klerkx L. Beyond agricultural innovation systems? Exploring an agricultural innovation ecosystems approach for niche design and development in sustainability transitions. Agric Syst, 2018, 164: 116-121.

[90] Power AG. Ecosystem services and agriculture: tradeoffs and synergies. Philos Trans Royal Soc B Biol Sci, 2010, 365(1554): 2959-2971.

[91] Rasouly I, Nabiollahi K, Taghizadeh R. Digital mapping of soil quality index (Case study; Ghorveh, Kurdistan Province). J Soil Manag Sustain Prod, 2020, 10(1): 101-118.

[92] Rawls WJ, Brakensiek DL. Estimating soil water retention from soil properties. J Irrig Drain Div., 1982, 108(2): 166-171.

[93] Rawls WJ, Pachepsky Y, Ritchie JC, Sobecki TM, Bloodworth H. Effect of soil carbon on soil water retention. Geoderma, 2003, 116(1–2): 61-76.

[94] Rodriguez-Moreno F, Kren J, Zemek F, Novak J, Lukas V, Pikl M. Advantage of multispectral imaging with sub-centimeter resolution in precision agriculture: generalization of training for supervised classification. Precis Agric, 2017, 18(4): 615-634.

[95] Rossel RAV, Fouad Y, Walter C. Using a digital camera to measure soil organic carbon and iron contents. Biosys Eng, 2008, 100(2): 149-159.

[96] Saravanan K, Saraniya S. Cloud IOT based novel livestock monitoring and identification system using UID. Sensor Rev, 2018, 38(1): 21-33.

[97] Sarkhot DV, Grunwald S, Ge Y, Morgan CLS. Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy. Geoderma, 2011, 164(1–2): 22-32.

[98] Schiefer J, Lair GJ, Blum WEH. Potential and limits of land and soil for sustainable intensification of European agriculture. Agr Ecosyst Environ, 2016, 230: 283-293.

[99] Shepherd KD, Walsh MG. Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J, 2002, 66(3): 988-998.

[100] Staff SS. Soil survey manual, 1951, Washington: USDA, 503.

[101] Stockmann U, Adams MA, Crawford JW, Field DJ, Henakaarchchi N, Jenkins M, Minasny B, McBratney AB, de Courcelles VD, Singh K, Wheeler I, Abbott L, Angers DA, Baldock J, Bird M, Brookes PC, Chenu C, Jastrowh JD, Lal R, Lehmann J, O’Donnell AG, Parton WJ, Whitehead D, Zimmermann M. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agr Ecosyst Environ, 2013, 164: 80-99.

[102] Stockmann U, Minasny B, McBratney AB. How fast does soil grow?. Geoderma, 2014, 216: 48-61.

[103] Taylor MD, Kim ND, Hill RB, Chapman R. A review of soil quality indicators and five key issues after 12 yr soil quality monitoring in the Waikato region. Soil Use Manag, 2010, 26(3): 212-224.

[104] Tobler, W.R. 1970. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(ArticleType: research-article/Issue Title: Supplement: Proceedings. International Geographical Union. Commission on Quantitative Methods/Full publication date: Jun., 1970/Copyright © 1970 Clark University), 234–240

[105] Toni M, Manciocco A, Angiulli E, Alleva E, Cioni C, Malavasi S. Review: assessing fish welfare in research and aquaculture, with a focus on European directives. Animal, 2019, 13(1): 161-170.

[106] Topp GC, Ferre PA (2002) Water content. In: Methods of Soil Analysis: Part 4 Physical Methods, (Eds.) J.H. Dane, G.C. Topp, Vol. 5, SSSA, Agronomy Monograph. Madison, WI, pp. 417–422

[107] Tranter G, McBratney AB, Minasny B. Using distance metrics to determine the appropriate domain of pedotransfer function predictions. Geoderma, 2009, 149(3): 421-425.

[108] U.N. 2019. Project Breakthrough: Digital Agriculture. in: Disruptive Technology Executive Briefs, United Nations Global Compact. breakthrough.unglobalcompact.org, pp. breakthrough.unglobalcompact.org

[109] Venegas-Li R, Levin N, Morales-Barquero L, Kaschner K, Garilao C, Kark S. Global assessment of marine biodiversity potentially threatened by offshore hydrocarbon activities. Glob Change Biol, 2019, 25(6): 2009-2020.

[110] Watanabe WO, Losordo TM, Fitzsimmons K, Hanley F. Tilapia production systems in the Americas: technological advances, trends, and challenges. Rev Fish Sci, 2002, 10(3–4): 465-498.

[111] Weersink A, Fraser E, Pannell D, Duncan E, Rotz S. Opportunities and challenges for big data in agricultural and environmental analysis. Ann Rev Resou Econ, 2018, 10: 19-37.

[112] West TO, Post WM. Soil organic carbon sequestration rates by tillage and crop rotation. Soil Sci Soc Am J., 2002, 66(6): 1930-1946.

[113] Wienhold BJ, Andrews SS, Karlen DL. Soil quality: a review of the science and experiences in the USA. Environ Geochem Health, 2004, 26(2–3): 89-95.

[114] Wyckhuys KAG, Bentley JW, Lie R, Nghiem LTP, Fredrix M. Maximizing farm-level uptake and diffusion of biological control innovations in today’s digital era. Biocontrol, 2018, 63(1): 133-148.

[115] Yemefack M, Jetten VG, Rossiter DG. Developing a minimum data set for characterizing soil dynamics in shifting cultivation systems. Soil Tillage Res, 2006, 86(1): 84-98.

[116] Zeraatpisheh M, Bakhshandeh E, Hosseini M, Alavi SM. Assessing the effects of deforestation and intensive agriculture on the soil quality through digital soil mapping. Geoderma, 2020, 363: 114139.