Abstract
Background and Aims
To enhance Brazilian sugarcane production sustainably, crop simulation models have been utilized. However, due to the lack of reliable information, particularly concerning soil variability, these models have shown limited performance for specific analyses. This study aims to evaluate Digital Soil Mapping (DSM) as an alternative for filling soil data gaps in crop modeling and to assess the influence of these products on prediction uncertainties. The study site is located in Piracicaba region, Southern Brazil.
Methods
The framework was: (i) a legacy soil data were utilized, and equal-spline equations were applied to standardize the dataset.; (ii) a machine learning (ML) algorithm was used to predict soil attributes and their uncertainties; (iii) pedotransfer functions were applied to obtain soil hydrological properties; (iv) DSSAT/CANEGRO crop model was used to estimate sugarcane yield; (iv) a legacy soil map (LSM), SoilGrids (SG) and a map of attributes derived from regional DSM (RDSM) were compared; (v) a Monte Carlo Simulation (MCS) was conducted with the RDSM maps to evaluate the impact of uncertainties in the estimation of sugarcane yield.
Results
The DSM proved to be a reliable source for use in crop models, reaching similar results to field data. The sugarcane yield map emphasized the model’s sensitivity to soil attributes, with texture and depth significantly impacting yield estimations.
Conclusion
In this sense, coupling DSM and crop modeling is a feasible way to improve yield estimates, especially in countries with limited soil databases.
Highlights
• Crop simulation models have limited application due to the lack of soil data.
• Digital Soil Mapping was coupled to a sugarcane simulation model to fill the gap of soil information.
• Soil attributes and their uncertainties were predicted on a 250-m grid using machine learning algorithm.
• A spatially-explicit DSSAT/CANEGRO model was able to represent variations in sugarcane yield at the regional scale;
• Sugarcane yield was strongly affected by soil variability and its uncertainties;
• Our finds indicate the importance of detailed soil databases and their impact on yield predictions.
AbstractSection Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not applicable.
Code availability
Not applicable.
References
Alderman PD (2020) A comprehensive R interface for the DSSAT cropping systems model. Comput Electron Agric 172:105325. https://doi.org/10.1016/j.compag.2020.105325
Alvares CA, Stape JL, Sentelhas PC, de Moraes Gonçalves JL, Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorol Z 711–728. https://doi.org/10.1127/0941-2948/2013/0507
Baigorria G, Jones J, Shin D, Mishra A, O’Brien J (2007) Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Clim Res 34:211–222. https://doi.org/10.3354/cr00703
Basso B, Ritchie JT, Pierce FJ, Braga RP, Jones JW (2001) Spatial validation of crop models for precision agriculture. Agric Syst 68:97–112. https://doi.org/10.1016/S0308-521X(00)00063-9
Batjes N, Ribeiro E, van Oostrum A (2019) Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth Syst Sci Data Discuss 1–46. https://doi.org/10.5194/essd-2019-164
Battie Laclau P, Laclau JP (2009) Growth of the whole root system for a plant crop of sugarcane under rainfed and irrigated environments in Brazil. Field Crop Res 114:351–360. https://doi.org/10.1016/j.fcr.2009.09.004
Bishop TFA, McBratney AB, Laslett GM (1999) Modeling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma 91:27–45. https://doi.org/10.1016/S0016-7061(99)00003-8
Bordonal R, de Carvalho O, Lal JLN, de Figueiredo R, de Oliveira EB, La Scala BG (2018) Sustainability of sugarcane production in Brazil. a review. Agron Sustain Dev. https://doi.org/10.1007/s13593-018-0490-x
Botula YD, Van Ranst E, Cornelis WM (2014) Funções De pedotransferência para predizer a retenção de água de solos dos trópicos úmidos: Uma revisão. Rev Bras Cienc do Solo 38:679–698. https://doi.org/10.1590/S0100-06832014000300001
Brasil (1981) Ministério das Minas e Energia. Secretaria Geral. Projeto RADAMBRASIL. Folha SD.22 - Goiás, SD.23 - Brasília, SE.22 - Goiânia. (Levantamento de Recursos Naturais, 25, 29, 31). Rio de Janeiro
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Brogi C, Huisman JA, Weihermüller L, Herbst M, Vereecken H (2021) Added value of geophysics-based soil mapping in agro-ecosystem simulations. Soil 7:125–143. https://doi.org/10.5194/soil-7-125-2021
Camargo AP, Sentelhas PC (1997) Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no Estado de São Paulo, 5, Rev Bras Agrometeorol, Brasil, pp 89-97
Camargo OA, Moniz AC, Jorge JA, Valadares JMAS (2009) Métodos de Análise Química Mineralógica e Física de Solos do Instituto Agronômico de Campinas, Campinas
Carvalho JLN, Nogueirol RC, Menandro LMS, Bordonal RdeO, Borges CD, Cantarella H, Franco HCJ (2017) Agronomic and environmental implications of sugarcane straw removal: a major review. GCB Bioenergy. https://doi.org/10.1111/gcbb.12410
Chen F, Kissel DE, West LT, Adkins W, Rickman D, Luvall JC (2008) Mapping soil organic carbon concentration for multiple fields with image similarity analysis. Soil Sci Soc Am J 72:186–193. https://doi.org/10.2136/sssaj2007.0028
CONAB (2020) Acompanhamento da Safra Brasileira: Cana-de-açúcar. v.4 Safra 2020/19 n.3 - Terceiro levantamento. Companhia Nacional de Abastecimento
Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J (2015) System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci Model Dev 8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
Demattê JLI, Demattê JAM (2009) Ambientes de produção como estratégia de manejo na cultura de cana-de-açúcar. Informações Agronômicas, pp 10–18
Demattê JAM, Fongaro CT, Rizzo R, Safanelli JL (2018) Geospatial soil sensing system (GEOS3): a powerful data mining procedure to retrieve soil spectral reflectance from satellite images. Remote Sens Environ 212:161–175. https://doi.org/10.1016/J.RSE.2018.04.047
Demattê JAM, Safanelli JL, Poppiel RR, Rizzo R, Silvero NEQ, Mendes WdeS, Bonfatti BR, Dotto AC, Salazar DFU, Mello FA, de Paiva O, da Souza AF, Santos AB, dos Nascimento NV, Mello C, de Bellinaso DC, Gonzaga Neto H, Amorim L, Resende MTA, de Vieira MEB, Queiroz JdaS, de Gallo LG, Sayão BC, Lisboa VM (2020) Bare earth’s surface spectra as a proxy for soil resource monitoring. Sci Rep 10:1–11. https://doi.org/10.1038/s41598-020-61408-1
Dharumarajan S, Vasundhara R, Suputhra A, Lalitha M, Hegde R (2020) Prediction of soil depth in Karnataka using digital soil mapping approach. J Indian Soc Remote Sens 48:1593–1600. https://doi.org/10.1007/s12524-020-01184-7
Dias HB, Sentelhas PC (2017) Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields. Field Crop Res 213:174–185. https://doi.org/10.1016/j.fcr.2017.07.022
Dias HB, Sentelhas PC (2018) Sugarcane yield gap analysis in Brazil – A multi-model approach for determining magnitudes and causes. Sci Total Environ 637–638:1127–1136. https://doi.org/10.1016/J.SCITOTENV.2018.05.017
Folberth C, Elliott J, Müller C, Balkovic J, Chryssanthacopoulos J, Izaurralde RC, Jones CD, Khabarov N, Liu W, Reddy A, Schmid E, Skalský R, Yang H, Arneth A, Ciais P, Deryng D, Lawrence PJ, Olin S, Pugh TAM, Ruane AC, Wang X (2016a) Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates. Biogeosci Discuss. 1–30. https://doi.org/10.5194/bg-2016-527
Folberth C, Skalský R, Moltchanova E, Balkovič J, Azevedo LB, Obersteiner M, van der Velde M (2016b) Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat Commun 7:11872. https://doi.org/10.1038/ncomms11872
Fongaro C, Demattê J, Rizzo R, Lucas Safanelli J, Mendes W, Dotto A, Vicente L, Franceschini M, Ustin S, Fongaro CT, Demattê JAM, Rizzo R, Safanelli L, Mendes J, Dotto WDS, Vicente AC, Franceschini LE, Ustin MHD (2018) Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images. Remote Sens 10:1555. https://doi.org/10.3390/rs10101555
Gallo B, Demattê J, Rizzo R, Safanelli J, Mendes W, Lepsch I, Sato M, Romero D, Lacerda M (2018) Multi-temporal satellite images on topsoil attribute quantification and the relationship with soil classes and geology. Remote Sens 10:1571. https://doi.org/10.3390/rs10101571
Gerland P, Raftery AE, Sevčíková H, Li N, Gu D, Spoorenberg T, Alkema L, Fosdick BK, Chunn J, Lalic N, Bay G, Buettner T, Heilig GK, Wilmoth J (2014) World population stabilization unlikely this century. Science 346:234–237. https://doi.org/10.1126/science.1257469
Gijsman AJ, Thornton PK, Hoogenboom G (2007) Using the WISE database to parameterize soil inputs for crop simulation models. Comput Electron Agric 56:85–100. https://doi.org/10.1016/j.compag.2007.01.001
Guo C, Zhang L, Zhou X, Zhu Y, Cao W, Qiu X, Cheng T, Tian Y (2018) Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning. Precis Agric 19:55–78. https://doi.org/10.1007/s11119-017-9498-5
Han E, Ines AVM, Koo J (2019) Development of a 10-km resolution global soil profile dataset for crop modeling applications. Environ Model Softw 119:70–83. https://doi.org/10.1016/J.ENVSOFT.2019.05.012
Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B, Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenaars JGB, Ribeiro E, Wheeler I, Mantel S, Kempen B (2017) SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12:e0169748. https://doi.org/10.1371/journal.pone.0169748
Hoogenboom G, Porter CH, Boote KJ, Shelia V, Wilkens PW, Singh U, White JW, Asseng S, Lizaso JI, Moreno LP, Pavan W, Ogoshi R, Hunt LA, Tsuji GY, Jones JW (2019) The DSSAT crop modeling ecosystem. In: p.173–216 [K.J. Boote, editor] advances in crop modeling for a sustainable agriculture. Burleigh Dodds Science Publishing, Cambridge. https://doi.org/10.19103/AS.2019.0061.10)
Inman-Bamber NG, Smith DM (2005) Water relations in sugarcane and response to water deficits. Field Crops Res. Elsevier, pp 185–202. https://doi.org/10.1016/j.fcr.2005.01.023
IUSS Working Group (2006) WRB World reference base for soil resources World Soil Resources Report, 103
Jones CA, Kiniry JR, (1986) Ceres-Maize: a simulation model of maize growth and development. College Station: Texas A & M University Press,
Jones J, Hoogenboom G, Porter C, Boote K, Batchelor W, Hunt L, Wilkens P, Singh U, Gijsman A, Ritchie J (2003) The DSSAT cropping system model. Eur J Agron 18:235–265. https://doi.org/10.1016/S1161-0301(02)00107-7
Jones JW, Antle JM, Basso B, Boote KJ, Conant RT, Foster I, Godfray HCJ, Herrero M, Howitt RE, Janssen S, Keating BA, Munoz-Carpena R, Porter CH, Rosenzweig C, Wheeler TR (2017) Brief history of agricultural systems modeling. Agric Syst 155:240–254. https://doi.org/10.1016/J.AGSY.2016.05.014
Jones M, Singels A (2008) DSSAT v4. 5 Canegro sugarcane plant module: user documentation.
Karthikeyan L, Chawla I, Mishra AK (2020) A review of remote sensing applications in agriculture for food security: crop growth and yield, irrigation, and crop losses. J Hydrol 586:124905. https://doi.org/10.1016/j.jhydrol.2020.124905
Khaledian Y, Miller BA (2020) Selecting appropriate machine learning methods for digital soil mapping. Appl Math Model 81:401–418. https://doi.org/10.1016/j.apm.2019.12.016
Keating BA, Robertson MJJ, Muchow RCC, Huth NII (1999) Modelling sugarca 25 production systems I. Development and performance of the sugarcane module. F Crop Res 61:253–271. https://doi.org/10.1016/S0378-4290(98)00167-1
Lagacherie P, Cazemier DR, Martin-Clouaire R, Wassenaar T (2000) A spatial approach using imprecise soil data for modelling crop yields over vast areas. Agric Ecosyst Environ 81:5–16. https://doi.org/10.1016/S0167-8809(00)00164-X
Landell MG, de Prado A, de Vasconcelos H, Perecin ACM, Rossetto D, Bidoia R, Silva MAP, de Xavier M (2003) Atributos químicos de subsuperfície de latossolos e produtividades da cana-de-açúcar. Sci Agric 60:741–745. https://doi.org/10.1590/S0103-90162003000400020
Launay M, Guerif M (2005) Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric Ecosyst Environ 111:321–339. https://doi.org/10.1016/J.AGEE.2005.06.005
Liang Y, Yu S, Zhen Z, Zhao Y, Deng J, Jiang W (2020) Climatic change impacts on Chinese sugarcane planting: benefits and risks. Phys Chem Earth 116:102856. https://doi.org/10.1016/j.pce.2020.102856
Ließ M, Gebauer A, Don A (2021) Machine learning with GA optimization to model the agricultural soil-landscape of Germany: an approach involving soil functional types with their multivariate parameter distributions along the depth profile. Front Environ Sci 9. https://doi.org/10.3389/fenvs.2021.692959
Machwitz M, Hass E, Junk J, Udelhoven T, Schlerf M (2019) CropGIS – a web application for the spatial and temporal visualization of past, present and future crop biomass development. Comput Electron Agric 161:185–193. https://doi.org/10.1016/J.COMPAG.2018.04.026
Malone BP, McBratney AB, Minasny B, Laslett GM (2009) Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 154:138–152. https://doi.org/10.1016/j.geoderma.2009.10.007
Malone BP, McBratney AB, Minasny B (2011) Empirical estimates of uncertainty for mapping continuous depth functions of soil attributes. Geoderma 160:614–626. https://doi.org/10.1016/j.geoderma.2010.11.013
Malone BP, Minansy B, Brungard C (2019) Some methods to improve the utility of conditioned Latin hypercube sampling. PeerJ 2019. https://doi.org/10.7717/peerj.6451
Marin FR, Jones JW, Singels A, Royce F, Assad ED, Pellegrino GQ, Justino F (2013) Climate change impacts on sugarcane attainable yield in southern Brazil. Clim Change 117:227–239. https://doi.org/10.1007/s10584-012-0561-y
McBratney AB, Mendonça Santos ML, Minasny B, McBratney AB, Mendonça Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117:3–52. https://doi.org/10.1016/S0016-7061(03)00223-4
Mendes WdeS, Neto M, Demattê LG, Gallo JAM, Rizzo BC, Safanelli R, Fongaro JL (2019) Is it possible to map subsurface soil attributes by satellite spectral transfer models? Geoderma 343:269–279. https://doi.org/10.1016/j.geoderma.2019.01.025
Mezzalira S (1965) Descrição geológica e geográfica das folhas de Piracicaba e São Carlos. Instituto Geográfico e Geológico, São Paulo, p 37
Miao Y, Mulla DJ, Batchelor WD, Paz JO, Robert PC, Wiebers M (2006) Evaluating management zone optimal nitrogen rates with a crop growth model. Agron J 98:545–553. https://doi.org/10.2134/AGRONJ2005.0153
Miller RO, Kissel DE (2010) Comparison of soil pH methods on soils of North America. Soil Sci Soc Am J 74:310–316. https://doi.org/10.2136/sssaj2008.0047
Monteiro LA, Sentelhas PC (2017) Sugarcane yield gap: Can it be determined at national level with a simple agrometeorological model? Crop Pasture Sci 68:272–284. https://doi.org/10.1071/CP16334
Nendel C, Wieland R, Mirschel W, Specka X, Guddat C, Kersebaum KC (2013) Simulating regional winter wheat yields using input data of different spatial resolution. Field Crop Res 145:67–77. https://doi.org/10.1016/j.fcr.2013.02.014
Nolasco de Carvalho CC, Nunes FC, Homem Antunes MA, Nolasco MC (2015) Soil surveys in Brazil and perspectives in soil digital mapping. Soil Horizons 56:0. https://doi.org/10.2136/sh14-01-0002
Oliveira JB, Prado H (1989) Carta Pedológica Semi-detalhada do Estado de São Paulo: Quadrícula de Piracicaba. Folha SF‐23‐Y‐A‐IV. Instituto Agronômico de Campinas, Campinas
Ottoni MV, Filho O, Schaap TB, Lopes-Assad MG, Filho MLRCR (2018) Hydrophysical database for Brazilian soils (HYBRAS) and pedotransfer functions for water retention. Vadose Zone J 17:170095. https://doi.org/10.2136/vzj2017.05.0095
Pagani V, Stella T, Guarneri T, Finotto G, van den Berg M, Marin FR, Acutis M, Confalonieri R (2017) Forecasting sugarcane yields using agro-climatic indicators and Canegro model: a case study in the main production region in Brazil. Agric Syst 154:45–52. https://doi.org/10.1016/j.agsy.2017.03.002
Péné CB, N’diaye S, N’guessan-Konan C (2012) Sprinkler irrigation and soil tillage practices in sugarcane plantations as influenced by soil texture and water storage in northern Ivory Coast. J Appl Biosci 54:3916–3924
Poggio L, de Sousa LM, Batjes NH, Heuvelink GBM, Kempen B, Ribeiro E, Rossiter D (2021) SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil 7:217–240. https://doi.org/10.5194/soil-7-217-2021
Pouillot R, Delignette-Muller ML (2010) Evaluating variability and uncertainty separately in microbial quantitative risk assessment using two R packages. Int J Food Microbiol 142:330–340. https://doi.org/10.1016/j.ijfoodmicro.2010.07.011
Python Software Foundation (2018) Python language reference, Version 3.7. Available online: https://www.python.org/
R Core Team (2018) R: A Language and Environment for Statistical Computing. Available online: https://www.r-project.org/
Ritchie JT, Godwin DC, Singh U (1990) Soil and weather inputs for the IBSNAT crop models. International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) Project Proceedings of the IBSNAT Symposium: Decision Support System for Agrotechnology Transfer. Part I. Symposium Proceedings, Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honolulu, Hawaii, Las Vegas, NV, October 16–18, 1989
Rizzo R, Medeiros LG, de Mello DC, Marques KPP, Mendes WdeS, Quiñonez Silvero NE, Dotto AC, Bonfatti BR, Demattê JAM (2020) Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil. Geoderma 361:114018. https://doi.org/10.1016/j.geoderma.2019.114018
Runyan CW, Stehm J (2019) Land use change, deforestation and competition for land due to food production. Encycl Food Secur Sustain 21–26. https://doi.org/10.1016/B978-0-08-100596-5.21995-1
Ryczek M, Kruk E, Malec M, Klatka S (2017) Comparison of pedotransfer functions for the determination of saturated hydraulic conductivity coefficient. Environ Prot Nat Resour 28:25–30. https://doi.org/10.1515/oszn-2017-0005
Sachin G, Mohammed Ahamed J, Nagajothi K, Rana M, Murugan BS (2019) Automation of the DSSAT crop growth simulation model, in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. International Society for Photogrammetry and Remote Sensing, pp 251–256. https://doi.org/10.5194/isprs-archives-XLII-3-W6-251-2019
Sadler EJ, Gerwig BK, Evans DE, Busscher WJ, Bauer PJ (2000) Site-specific modeling of corn yield in the SE coastal plain. Agric Syst 64:189–207. https://doi.org/10.1016/S0308-521X(00)00022-6
Safanelli JL, Chabrillat S, Ben-Dor E, Demattê JAM (2020) Multispectral models from bare soil composites for mapping topsoil properties over Europe. Remote Sens 12:1369. https://doi.org/10.3390/RS12091369
Sanches GM, Graziano Magalhães PS, Franco J (2019) Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes. Geoderma 334:90–98. https://doi.org/10.1016/j.geoderma.2018.07.051
Scull P, Franklin J, Chadwick OA, McArthur D (2003) Predictive soil mapping: a review. Prog Phys Geogr Earth Environ 27:171–197. https://doi.org/10.1191/0309133303pp366ra
Sentelhas PC, Battisti R, Câmara GMS, Farias JRB, Hampf AC, Nendel C (2015) The soybean yield gap in Brazil - Magnitude, causes and possible solutions for sustainable production. J Agric Sci 153:1394–1411. https://doi.org/10.1017/S0021859615000313
Shelia V, Hansen J, Sharda V, Porter C, Aggarwal P, Wilkerson CJ, Hoogenboom G (2019) A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies. Environ Model Softw 115:144–154. https://doi.org/10.1016/J.ENVSOFT.2019.02.006
Singels A, van den Berg M, Smit MA, Jones MR, van Antwerpen R (2010) Modelling water uptake, growth and sucrose accumulation of sugarcane subjected to water stress. Field Crop Res 117:59–69. https://doi.org/10.1016/j.fcr.2010.02.003
Singels A, Jones M, Marin F et al (2014) Predicting climate change impacts on sugarcane production at sites in Australia, Brazil and South Africa using the Canegro Model. Sugar Tech 16:347–355. https://doi.org/10.1007/s12355-013-0274-1
Smith FP, Holzworth DP, Robertson MJ (2005) Linking icon-based models to code-based models: a case study with the agricultural production systems simulator. Agric Syst 83(2):135–151. https://doi.org/10.1016/j.agsy.2004.03.004
Stumpf F, Schmidt K, Goebes P, Behrens T, Schönbrodt-Stitt S, Wadoux A, Xiang W, Scholten T (2017) Uncertainty-guided sampling to improve digital soil maps. Catena 153:30–38. https://doi.org/10.1016/j.catena.2017.01.033
Tesfa TK, Tarboton DG, Chandler DG, McNamara JP (2009) Modeling soil depth from topographic and land cover attributes. Water Resour Res 45. https://doi.org/10.1029/2008WR007474
Tewes A, Hoffmann H, Nolte M, Krauss G, Schäfer F, Kerkhoff C, Gaiser T (2020) How do methods assimilating sentinel-2-derived LAI combined with two different sources of soil input data affect the crop model-based estimation of wheat biomass at sub-field level? Remote Sens 12:925. https://doi.org/10.3390/rs12060925
Thorp KR, DeJonge KC, Kaleita AL, Batchelor WD, Paz JO (2008) Methodology for the use of DSSAT models for precision agriculture decision support. Comput Electron Agric 64:276–285. https://doi.org/10.1016/j.compag.2008.05.022
Tifafi M, Guenet B, Hatté C (2018) Large differences in global and regional total soil carbon stock estimates based on soilGrids, HWSD, and NCSCD: Intercomparison and evaluation based on field data from USA, England, Wales, and France. Global Biogeochem. Cycles 32:42–56. https://doi.org/10.1002/2017GB005678
Tomasella J, Hodnett MG, Rossato L (2000) Pedotransfer functions for the estimation of soil water retention in Brazilian soils. Soil Sci Soc Am J 64:327–338. https://doi.org/10.2136/sssaj2000.641327x
Van Ittersum MK, Cassman KG, Grassini P, Wolf J, Tittonell P, Hochman Z (2013) Yield gap analysis with local to global relevance-A review. Field Crop Res 143:4–17. https://doi.org/10.1016/j.fcr.2012.09.009
Varella H, Guérif M, Buis S (2010) Global sensitivity analysis measures the quality of parameter estimation: the case of soil parameters and a crop model. Environ Model Softw 25:310–319. https://doi.org/10.1016/j.envsoft.2009.09.012
Vasques GM, Demattê JAM, Viscarra Rossel RA, Ramírez-López L, Terra FS (2014) Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths. Geoderma 223–225:73–78. https://doi.org/10.1016/j.geoderma.2014.01.019
Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O’Donnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90:8995. https://doi.org/10.1029/jc090ic05p08995
Wimalasiri EM, Jahanshiri E, Suhairi T, Mapa RB, Karunaratne AS, Vidhanarachchi LP, Azam-Ali SN (2020) Basic soil data requirements for process-based crop models as a basis for crop diversification. Sustain 12:7781. https://doi.org/10.3390/SU12187781
Zhao G, Bryan BA, King D, Luo Z, Wang E, Bende-Michl U, Song X, Yu Q (2013) Large-scale, high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing. Environ Model Softw 41:231–238. https://doi.org/10.1016/j.envsoft.2012.08.007
Acknowledgements
The authors would like to thank the Coordination for the Improvement of Higher Education Personnel for the first author scholarship and the São Paulo Research Foundation (FAPESP) for the scholarship of the second author and foundation support. We also thank the Geotechnologies on Soil Science Group - GEOCIS (esalqgeocis.wixsite.com/English). The authors also thank the joint project of Raízen Company with the Luiz de Queiroz Agricultural Studies Foundation. The authors dedicate this article to the memory of Paulo Cesar Sentelhas, who will always be remembered for his teachings as a professor and his contributions to the agrometeorology community.
Funding
This research was funded the Coordination for the Improvement of Higher Education Personnel (CAPES – Finance Code 001), the São Paulo Research Foundation (FAPESP) (Grants nº 2014-22262-0; 2018/23760-4) and the project of Raízen Company with the Luiz de Queiroz Agricultural Studies Foundation (Grant nº 87017).
Author information
Authors and Affiliations
Contributions
Natasha Valadares dos Santos and Rodnei Rizzo contributed to the study conception and design. Material preparation, data collection and analysis were performed by Natasha Valadares dos Santos, Rodnei Rizzo and Henrique Boriolo Dias. The first draft of the manuscript was written by Natasha Valadares dos Santos, Rodnei Rizzo, Henrique Boriolo Dias, Paulo Cesar Sentelhas and José Alexandre Melo Demattê.All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflicts of interest/Competing interests
The authors have no conflicts of interest to declare.
Additional information
Responsible Editor: Hans Lambers.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Below is the link to the electronic supplementary material.
ESM 1
(DOCX 112 KB)
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
dos Santos, N.V., Rizzo, R., Dias, H.B. et al. Digital soil mapping and crop modeling to define the spatially-explicit influence of soils on water-limited sugarcane yield. Plant Soil (2024). https://doi.org/10.1007/s11104-024-06587-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11104-024-06587-w