Assessment of the soil fertility status in Benin (West Africa) – Digital soil mapping using machine learning
Tài liệu tham khảo
Abate, 2016, Effects of land use, soil depth and topography on soil physicochemical properties along the toposequence at the Wadla Delanta Massif, Northcentral Highlands Ethiopia Environ. Pollut., 5
Adhikari, 2014, Digital mapping of soil organic carbon contents and stocks in Denmark, PLoS One, 9, 10.1371/journal.pone.0105519
Aholoukpè, 2011
Amonmide, 2019, Contribution à l’évaluation du niveau de fertilité des sols dans les systèmes de culture à base du coton au Bénin, Int. J. Biol. Chem. Sci., 13, 1846, 10.4314/ijbcs.v13i3.52
Amoussou, 2016
Arrouays, 2017, Soil legacy data rescue via GlobalSoilMap and other international and national initiatives, GeoResJ, 14, 1, 10.1016/j.grj.2017.06.001
Azuka, 2020, Land use and slope position effect on the hydrological properties of sandy loam soils of Koupendri catchment, North-West of Benin, Trop. Subtrop. Agroecosyst., 23, 10.56369/tsaes.2876
Behrens, 2014, Hyper-scale digital soil mapping and soil formation analysis, Geoderma, 213, 578, 10.1016/j.geoderma.2013.07.031
Blume, 2016, Threats to the soil functions, 485
Bonfatti, 2016, Digital mapping of soil carbon in a viticultural region of southern Brazil, Geoderma, 261, 204, 10.1016/j.geoderma.2015.07.016
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Breure, 2012, Ecosystem services: a useful concept for soil policy making!, Curr. Opin. Environ. Sustain., 4, 578, 10.1016/j.cosust.2012.10.010
Calzolari, 2016, A methodological framework to assess the multiple contributions of soils to ecosystem services delivery at regional scale, Geoderma, 261, 190, 10.1016/j.geoderma.2015.07.013
Čeh, 2018, Estimating the performance of random forest versus multiple regression for predicting prices of the apartments, ISPRS Int. J. Geo Inf., 7, 168, 10.3390/ijgi7050168
Challinor, 2007, Assessing the vulnerability of food crop systems in Africa to climate change, Clim. Chang., 83, 381, 10.1007/s10584-007-9249-0
Charzyński, 2017, Soil sealing influence on some microbiological biochemical and physicochemical properties of Ekranic Technosols of Toruń, SUITMA, 9
Cincotta, 2019, Soil aggregates as a source of dissolved organic carbon to streams: an experimental study on the effect of solution chemistry on water extractable carbon, Front. Environ. Sci., 7, 172, 10.3389/fenvs.2019.00172
Conrad, 2015, System for automated geoscientific analyses (SAGA) v. 2.1. 4, Geosci. Model Dev., 8, 1991, 10.5194/gmd-8-1991-2015
Dansi, A., Vodouhè, R., Azokpota, P., Yedomonhan, H., Assogba, P., Adjatin, A., Loko, Y., Dossou-Aminon, I., Akpagana, K.J.T.S.W.J., 2012. Diversity of the neglected and underutilized crop species of importance in Benin. Sci. World J. 2012.
De Groot, 2010, Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making, Ecol. Complex., 7, 260, 10.1016/j.ecocom.2009.10.006
Dharumarajan, 2017, Spatial prediction of major soil properties using random Forest techniques-a case study in semi-arid tropics of South India, Geoderma Reg., 10, 154, 10.1016/j.geodrs.2017.07.005
Dossou-Yovo, 2016, Combining no-tillage, rice straw mulch and nitrogen fertilizer application to increase the soil carbon balance of upland rice field in northern Benin, Soil Tillage Res., 163, 152, 10.1016/j.still.2016.05.019
Flynn, 2019, High-resolution digital soil mapping of multiple soil properties: an alternative to the traditional field survey?, South African J. Plant Soil, 36, 237, 10.1080/02571862.2019.1570566
Forkuor, 2017, High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models, PLoS One, 12, 10.1371/journal.pone.0170478
Gomes, 2019, Modelling and mapping soil organic carbon stocks in Brazil, Geoderma, 340, 337, 10.1016/j.geoderma.2019.01.007
Grant, 2016, 1
Greiner, 2017, Soil function assessment: review of methods for quantifying the contributions of soils to ecosystem services, Land Use Policy, 69, 224, 10.1016/j.landusepol.2017.06.025
Günal, 2015, Threats to sustainability of soil functions in central and Southeast Europe, Sustainability, 7, 2161, 10.3390/su7022161
Hengl, 2015, Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions, PLoS One, 10, e0125814, 10.1371/journal.pone.0125814
Hengl, 2017, SoilGrids250m: global gridded soil information based on machine learning, PLoS One, 12, 10.1371/journal.pone.0169748
Hengl, 2017, Soil nutrient maps of sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning, Nutr. Cycl. Agroecosyst., 109, 77, 10.1007/s10705-017-9870-x
Hien, 2006, Carbon sequestration in a savannah soil in southwestern Burkina as affected by cropping and cultural practices, Arid Land Res. Manag., 20, 133, 10.1080/15324980500546007
Holleran, 2015, Quantifying soil and critical zone variability in a forested catchment through digital soil mapping, Soil, 1, 47, 10.5194/soil-1-47-2015
Holmes, 1999, 1
Hopkins, 1987, Vegetation Map of Africa, A Descriptive Memoir to Accompany the Unesco/AETFAT/UNSO Vegetation map of Africa. JSTOR. The Vegetation of Africa
Horning, 2010, Random Forests: An algorithm for image classification and generation of continuous fields data sets, vol. 911
Hounkpatin, 2018, Predicting reference soil groups using legacy data: a data pruning and random Forest approach for tropical environment (Dano catchment, Burkina Faso), Sci. Rep., 8, 9959, 10.1038/s41598-018-28244-w
Hounkpatin, 2018, Soil organic carbon stocks and their determining factors in the Dano catchment (Southwest Burkina Faso), Catena, 166, 298, 10.1016/j.catena.2018.04.013
Hounnou, 2018, Variability of temperature, precipitation and potential evapotranspiration time series analysis in Republic of Benin, IJAER, 4, 991
Hu, 2020, Estimating Forest stock volume in Hunan Province, China, by integrating in situ plot data, Sentinel-2 images, and linear and machine learning regression models, Remote Sens., 12, 186, 10.3390/rs12010186
Igué, 2014, Projet « Evaluation du statut nutritionnel des sols des différentes zones agroécologiques du Bénin ». Rapport technique final
Igue, 2013, 12
Igue, 2016, Détermination Des Formules D’engrais Minéraux Et Organiques Sur Deux Types De Sols Pour Une Meilleure Productivité De Maïs (Zea mays l.) Dans La Commune De Banikoara (Nord-Est Du Bénin), Eur. Sci. J., 12, 16
Igué, 2018, Springer, 105
INSAE, 2017
Kasraei, 2021, Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning, Environ. Model Softw., 144, 10.1016/j.envsoft.2021.105139
Kempen, 2019, Mapping topsoil organic carbon concentrations and stocks for Tanzania, Geoderma, 337, 164, 10.1016/j.geoderma.2018.09.011
Kogo, 2019, Modelling climate suitability for rainfed Maize cultivation in Kenya using a Maximum Entropy (MaxENT) approach, Agronomy, 9, 727, 10.3390/agronomy9110727
Koné, 2010, Effet de différentes sources de phosphate sur le rendement du riz sur sols acides, Agron. Afr., 22, 55
Kouelo, 2016, Soil conservation practices in three watersheds of Benin: farmers cropping systems characterization, Afr. J. Agric. Res., 11, 507, 10.5897/AJAR2015.10277
Kuhn, 2013, 26
Kuhn
Kumhálová, 2011, The impact of topography on soil properties and yield and the effects of weather conditions, Precis. Agric., 12, 813, 10.1007/s11119-011-9221-x
Lawrence, 1989, A concordance correlation coefficient to evaluate reproducibility, Biometrics, 255
Leenaars, 2013
Leenaars, 2012, 3
Leenaars, 2014, 51
Leenaars, 2018, Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa, Geoderma, 324, 18, 10.1016/j.geoderma.2018.02.046
Li, 2018, Mapping soil cation-exchange capacity using Bayesian Modeling and proximal sensors at the field scale, Soil Sci. Soc. Am. J., 82, 10.2136/sssaj2017.10.0356
Ma, 2017, Downscaling annual precipitation with TMPA and land surface characteristics in China, Int. J. Climatol., 37, 5107, 10.1002/joc.5148
Madena, 2012, Soil functions-Today's situation and further development under climate change, Erdkunde, 221, 10.3112/erdkunde.2012.03.03
Malone, 2011, Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes, Geoderma, 160, 614, 10.1016/j.geoderma.2010.11.013
McBratney, 2003, On digital soil mapping, Geoderma, 117, 3, 10.1016/S0016-7061(03)00223-4
Miller, 2015, Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks, Soil, 1, 217, 10.5194/soil-1-217-2015
Miller, 2015, Impact of multi-scale predictor selection for modeling soil properties, Geoderma, 239, 97, 10.1016/j.geoderma.2014.09.018
Minai, 2019
Minasny, 2008, Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy, Chemom. Intell. Lab. Syst., 94, 72, 10.1016/j.chemolab.2008.06.003
Minasny, 2016, Digital soil mapping: a brief history and some lessons, Geoderma, 264, 301, 10.1016/j.geoderma.2015.07.017
Mosleh, 2016, The effectiveness of digital soil mapping to predict soil properties over low-relief areas, Environ. Monit. Assess., 188, 195, 10.1007/s10661-016-5204-8
Nawar, 2015, Digital mapping of soil properties using multivariate statistical analysis and ASTER data in an arid region, Remote Sens., 7, 1181, 10.3390/rs70201181
Nelson, 2011, An error budget for different sources of error in digital soil mapping, Eur. J. Soil Sci., 62, 417, 10.1111/j.1365-2389.2011.01365.x
Neuenschwander, 2011
Padarian, 2019, Using deep learning to predict soil properties from regional spectral data, Geoderma Reg., 16
Phachomphon, 2010, Estimating carbon stocks at a regional level using soil information and easily accessible auxiliary variables, Geoderma, 155, 372, 10.1016/j.geoderma.2009.12.020
Piikki, 2015, Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal sensor data obtained from different sensing methods, Precis. Agric., 16, 29, 10.1007/s11119-014-9381-6
Poggio, 2019, SoilGrids: consistent soil information to assess and map soil functions at global scale, Wageningen soil conference 2019, ISRIC, 50
Poppiel, 2019, Mapping at 30 m resolution of soil attributes at multiple depths in Midwest Brazil, Remote Sens., 11, 2905, 10.3390/rs11242905
Ramcharan, 2018, Soil property and class maps of the conterminous United States at 100-meter spatial resolution, Soil Sci. Soc. Am. J., 82, 186, 10.2136/sssaj2017.04.0122
Saïdou, 2018, Fertilizer recommendations for maize production in the South Sudan and Sudano-Guinean zones of Benin, improving the profitability, sustainability and efficiency of nutrients through site specific fertilizer recommendations in West Africa agro-ecosystems, Springer, 215
Saiz, 2012, Variation in soil carbon stocks and their determinants across a precipitation gradient in West Africa, Glob. Chang. Biol., 18, 1670, 10.1111/j.1365-2486.2012.02657.x
Shahbazi, 2019, Evaluating the spatial and vertical distribution of agriculturally important nutrients—nitrogen, phosphorous and boron—in North West Iran, Catena, 173, 71, 10.1016/j.catena.2018.10.005
Silatsa, 2020, Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon, Geoderma, 367, 10.1016/j.geoderma.2020.114260
Solomatine, 2009, A novel method to estimate model uncertainty using machine learning techniques, Water Resour. Res., 45, 10.1029/2008WR006839
Somarathna, 2016, Mapping soil organic carbon content over New South Wales, Australia using local regression kriging, Geoderma Reg., 7, 38, 10.1016/j.geodrs.2015.12.002
Sonneveld, 2012, The impact of climate change on crop production in west Africa: an assessment for the oueme river basin in Benin, Water Resour. Manag., 26, 553, 10.1007/s11269-011-9931-x
Sulaeman, 2012, Soil-landscape models to predict soil pH variation in the Subang region of West Java, Indonesia, 317
Sys, 1976, Principes de classification et d’evaluation des terres pour la Republique Populaire du Benin, Rapport de la mission de
Szatmári, 2019, Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms, Geoderma, 337, 1329, 10.1016/j.geoderma.2018.09.008
Taghizadeh-Mehrjardi, 2014, Digital mapping of soil salinity in Ardakan region, central Iran, Geoderma, 213, 15, 10.1016/j.geoderma.2013.07.020
UN
Vasenev, 2018
Vaysse, 2017, Using quantile regression forest to estimate uncertainty of digital soil mapping products, Geoderma, 291, 55, 10.1016/j.geoderma.2016.12.017
Volkoff, 1976
Walkley, 1934, An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method, Soil Sci., 37, 29, 10.1097/00010694-193401000-00003
Wilson, 2017, Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland, Ecol. Appl., 27, 1646, 10.1002/eap.1557
Wubie, 2020, Effects of land cover changes and slope gradient on soil quality in the Gumara watershed, Lake Tana basin of North–West Ethiopia, Modeling Earth Syst. Environ., 6, 85, 10.1007/s40808-019-00660-5
Yigini, 2018
Zeraatpisheh, 2019, Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran, Geoderma, 338, 445, 10.1016/j.geoderma.2018.09.006