Machine learning as a useful tool for diagnosis of soil compaction under continuous no-tillage in Brazil
Devison Souza Peixoto![https://orcid.org/0000-0002-8093-2494](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0003-2750-5976](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-2604-0866](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-8240-8987](/media/client/orcid_16x16.png)
A Department of Soil Science, Federal University of Lavras, Avenida Doutor Sylvio Menicucci 1001, CEP 37200-000 Lavras, Minas Gerais, Brazil.
B Department of Agriculture, Federal University of Lavras, Avenida Doutor Sylvio Menicucci 1001, CEP 37200-000 Lavras, Minas Gerais, Brazil.
C Department of Agricultural Engineering, Federal University of Viçosa, Campus Universitário, CEP 36570-900 Viçosa, Minas Gerais, Brazil.
Soil Research 61(2) 145-158 https://doi.org/10.1071/SR22048
Submitted: 28 February 2022 Accepted: 8 August 2022 Published: 5 September 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Correct diagnosis of the state of soil compaction is a challenge in continuous no-tillage (NT).
Aims and methods: The aim of this study was to evaluate the performance of four machine learning algorithms to diagnose the state of soil compaction (NT and occasional tillage – OT). For these purposes, data from a field experiment conducted in a clayey Typic Hapludox with mechanical (chiselling and subsoiling) and chemical (gypsum and limestone) methods for mitigation of soil compaction were used. To diagnose the state of soil compaction, soil physical properties [soil bulk density, penetration resistance, macroporosity (MAC), microporosity (MIC), air capacity (AC), available water content, relative field capacity and total porosity (TP)] in addition to crop yield (Rel_Yield) were used as predictor variables for Classification and Regression Trees (CART), Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms.
Key results: The most important variables for predicting the state of soil compaction were Rel_Yield and soil porosity (MAC, TP, MIC and AC). The machine learning algorithms had satisfactory performance in diagnosing which sites were compacted and which were not. The decision tree algorithms (CART and RF) performed better than ANN and SVM, reaching accuracy = 0.90, Kappa index = 0.76 and sensitivity = 0.83.
Conclusions and implications: The machine learning algorithm approach proved to be an efficient tool in diagnosing soil compaction in continuous NT, improving decision-making concerning the use of OT.
Keywords: Artificial Neural Network, crop yield, decision tree, occasional tillage, Random Forest, soil physical properties, soil porosity, Support Vector Machine.
References
Arvidsson J, Håkansson I (2014) Response of different crops to soil compaction—short-term effects in Swedish field experiments. Soil and Tillage Research 138, 56–63.| Response of different crops to soil compaction—short-term effects in Swedish field experiments.Crossref | GoogleScholarGoogle Scholar |
Arvidsson J, Etana A, Rydberg T (2014) Crop yield in Swedish experiments with shallow tillage and no-tillage 1983–2012. European Journal of Agronomy 52, 307–315.
| Crop yield in Swedish experiments with shallow tillage and no-tillage 1983–2012.Crossref | GoogleScholarGoogle Scholar |
Behrens T, Förster H, Scholten T, Steinrücken U, Spies E-D, Goldschmitt M (2005) Digital soil mapping using artificial neural networks. Journal of Plant Nutrition and Soil Science 168, 21–33.
| Digital soil mapping using artificial neural networks.Crossref | GoogleScholarGoogle Scholar |
Bengough AG, Bransby MF, Hans J, McKenna SJ, Roberts TJ, Valentine TA (2006) Root responses to soil physical conditions; growth dynamics from field to cell. Journal of Experimental Botany 57, 437–447.
| Root responses to soil physical conditions; growth dynamics from field to cell.Crossref | GoogleScholarGoogle Scholar |
Blanco-Canqui H, Ruis SJ (2018) No-tillage and soil physical environment. Geoderma 326, 164–200.
| No-tillage and soil physical environment.Crossref | GoogleScholarGoogle Scholar |
Blanco-Canqui H, Wortmann CS (2020) Does occasional tillage undo the ecosystem services gained with no-till? A review. Soil and Tillage Research 198, 104534
| Does occasional tillage undo the ecosystem services gained with no-till? A review.Crossref | GoogleScholarGoogle Scholar |
Botta GF, Antille DL, Bienvenido F, Rivero D, Contessotto EE (2019) Energy requirements for alleviation of subsoil compaction and the effect of deep tillage on sunflower (Helianthus annus L.) yield in the western region of Argentina’s rolling pampa. Engineering for Rural Development 18, 174–178.
| Energy requirements for alleviation of subsoil compaction and the effect of deep tillage on sunflower (Helianthus annus L.) yield in the western region of Argentina’s rolling pampa.Crossref | GoogleScholarGoogle Scholar |
Breiman L (2001) Random forests. Machine Learning 45, 5–32.
| Random forests.Crossref | GoogleScholarGoogle Scholar |
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) ‘Classification and regression trees.’ (Taylor & Francis)
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 (2018) Soil quality – a critical review. Soil Biology and Biochemistry 120, 105–125.
| Soil quality – a critical review.Crossref | GoogleScholarGoogle Scholar |
Calonego JC, Rosolem CA (2010) Soybean root growth and yield in rotation with cover crops under chiseling and no-till. European Journal of Agronomy 33, 242–249.
| Soybean root growth and yield in rotation with cover crops under chiseling and no-till.Crossref | GoogleScholarGoogle Scholar |
Calonego JC, Raphael JPA, Rigon JPG, Oliveira Neto Ld, Rosolem CA (2017) Soil compaction management and soybean yields with cover crops under no-till and occasional chiseling. European Journal of Agronomy 85, 31–37.
| Soil compaction management and soybean yields with cover crops under no-till and occasional chiseling.Crossref | GoogleScholarGoogle Scholar |
Chlingaryan A, Sukkarieh S, Whelan B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Computers and Electronics in Agriculture 151, 61–69.
| Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review.Crossref | GoogleScholarGoogle Scholar |
Companhia Nacional de Abastecimento (2021) ‘Boletim da Safra de Grãos: Acompanhamento da safra 2020/21 brasileira de grãos - 8o levantamento.’ (CONAB: Brasília, DF) Available at https://www.conab.gov.br/
Correa J, Postma JA, Watt M, Wojciechowski T (2019) Soil compaction and the architectural plasticity of root systems. Journal of Experimental Botany 70, 6019–6034.
| Soil compaction and the architectural plasticity of root systems.Crossref | GoogleScholarGoogle Scholar |
Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random Forests for classification in ecology. Ecology 88, 2783–2792.
| Random Forests for classification in ecology.Crossref | GoogleScholarGoogle Scholar |
de Moura MS, Silva BM, Mota PK, Borghi E, Resende AVd, Acuña-Guzman SF, Araújo GSS, da Silva LdCM, de Oliveira GC, Curi N (2021) Soil management and diverse crop rotation can mitigate early-stage no-till compaction and improve least limiting water range in a Ferralsol. Agricultural Water Management 243, 106523
| Soil management and diverse crop rotation can mitigate early-stage no-till compaction and improve least limiting water range in a Ferralsol.Crossref | GoogleScholarGoogle Scholar |
Denardin JE, Faganello A, Santi A (2008) Falhas na implementação do sistema plantio direto levam à degradação do solo. Revista Plantio Direto 18, 3–4.
Deuschle D, Minella JPG, Hörbe TdAN, Londero AL, Schneider FJA (2019) Erosion and hydrological response in no-tillage subjected to crop rotation intensification in southern Brazil. Geoderma 340, 157–163.
| Erosion and hydrological response in no-tillage subjected to crop rotation intensification in southern Brazil.Crossref | GoogleScholarGoogle Scholar |
Drescher MS, Eltz FLF, Denardin JE, Faganello A (2011) Persistência do efeito de intervenções mecânicas para a descompactação de solos sob plantio direto. Revista Brasileira de Ciência do Solo 35, 1713–1722.
| Persistência do efeito de intervenções mecânicas para a descompactação de solos sob plantio direto.Crossref | GoogleScholarGoogle Scholar |
Drescher MS, Eltz FLF, Denardin JE, Faganello A, Drescher GL (2012) Resistência à penetração e rendimento da soja após intervenção mecânica em latossolo vermelho sob plantio direto. Revista Brasileira de Ciência do Solo 36, 1836–1844.
| Resistência à penetração e rendimento da soja após intervenção mecânica em latossolo vermelho sob plantio direto.Crossref | GoogleScholarGoogle Scholar |
Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: a survey. Computers and Electronics in Agriculture 155, 257–282.
| Forecasting yield by integrating agrarian factors and machine learning models: a survey.Crossref | GoogleScholarGoogle Scholar |
Everingham Y, Sexton J, Skocaj D, Inman-Bamber G (2016) Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development 36, 27
| Accurate prediction of sugarcane yield using a random forest algorithm.Crossref | GoogleScholarGoogle Scholar |
FEBRAPD (2019) Área do Sistema Plantio Direto. Federação Brasileira do Sistema Plantio Direto e Irrigação. Available at https://febrapdp.org.br/area-de-pd
Filippi P, Jones EJ, Wimalathunge NS, Somarathna PDSN, Pozza LE, Ugbaje SU, Jephcott TG, Paterson SE, Whelan BM, Bishop TFA (2019) An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precision Agriculture 20, 1015–1029.
| An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning.Crossref | GoogleScholarGoogle Scholar |
Fukuda S, Spreer W, Yasunaga E, Yuge K, Sardsud V, Müller J (2013) Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes. Agricultural Water Management 116, 142–150.
| Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes.Crossref | GoogleScholarGoogle Scholar |
Girardello VC, Amado TJC, Nicoloso RdS, Hörbe TdAN, Ferreira AdO, Tabaldi FM, Lanzanova ME (2011) Alterações nos atributos físicos de um latossolo vermelho sob plantio direto induzidas por diferentes tipos de escarificadores e o rendimento da soja. Revista Brasileira de Ciência do Solo 35, 2115–2126.
| Alterações nos atributos físicos de um latossolo vermelho sob plantio direto induzidas por diferentes tipos de escarificadores e o rendimento da soja.Crossref | GoogleScholarGoogle Scholar |
Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random Forests for land cover classification. Pattern Recognition Letters 27, 294–300.
| Random Forests for land cover classification.Crossref | GoogleScholarGoogle Scholar |
Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W (2014) Predictive ability of machine learning methods for massive crop yield prediction. Spanish Journal of Agricultural Research 12, 313–328.
| Predictive ability of machine learning methods for massive crop yield prediction.Crossref | GoogleScholarGoogle Scholar |
Hakojärvi M, Hautala M, Ristolainen A, Alakukku L (2013) Yield variation of spring cereals in relation to selected soil physical properties on three clay soil fields. European Journal of Agronomy 49, 1–11.
| Yield variation of spring cereals in relation to selected soil physical properties on three clay soil fields.Crossref | GoogleScholarGoogle Scholar |
Hamza MA, Anderson WK (2005) Soil compaction in cropping systems. Soil and Tillage Research 82, 121–145.
| Soil compaction in cropping systems.Crossref | GoogleScholarGoogle Scholar |
Jeong JH, Resop JP, Mueller ND, Fleisher DH, Yun K, Butler EE, Timlin DJ, Shim K-M, Gerber JS, Reddy VR, Kim S-H (2016) Random Forests for global and regional crop yield predictions. PLoS One 11, e0156571
| Random Forests for global and regional crop yield predictions.Crossref | GoogleScholarGoogle Scholar |
Kassam A, Friedrich T, Derpsch R (2018) Global spread of Conservation Agriculture. International Journal of Environmental Studies 76, 29–51.
| Global spread of Conservation Agriculture.Crossref | GoogleScholarGoogle Scholar |
Keller T, Sandin M, Colombi T, Horn R, Or D (2019) Historical increase in agricultural machinery weights enhanced soil stress levels and adversely affected soil functioning. Soil and Tillage Research 194, 104293
| Historical increase in agricultural machinery weights enhanced soil stress levels and adversely affected soil functioning.Crossref | GoogleScholarGoogle Scholar |
Khanal S, Fulton J, Klopfenstein A, Douridas N, Shearer S (2018) Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Computers and Electronics in Agriculture 153, 213–225.
| Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield.Crossref | GoogleScholarGoogle Scholar |
Kim N, Lee Y-W (2016) Machine learning approaches to corn yield estimation using satellite images and climate data: a case of Iowa State. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 34, 383–390.
| Machine learning approaches to corn yield estimation using satellite images and climate data: a case of Iowa State.Crossref | GoogleScholarGoogle Scholar |
Klute A (1986) Water retention: laboratory methods. In ‘Methods of soil analysis. Part 1. Physical and mineralogical methods’. SSSA Book Series. (Ed. A Klute) pp. 635–662. (SSSA)
Kuhn M (2008) Building predictive models in R using the caret package. Journal of Statistical Software 28, 1–26.
| Building predictive models in R using the caret package.Crossref | GoogleScholarGoogle Scholar |
Lehmann J, Bossio DA, Kögel-Knabner I, Rillig MC (2020) The concept and future prospects of soil health. Nature Reviews Earth & Environment 1, 544–553.
| The concept and future prospects of soil health.Crossref | GoogleScholarGoogle Scholar |
Lipiec J, Stępniewski W (1995) Effects of soil compaction and tillage systems on uptake and losses of nutrients. Soil and Tillage Research 35, 37–52.
| Effects of soil compaction and tillage systems on uptake and losses of nutrients.Crossref | GoogleScholarGoogle Scholar |
Lipiec J, Horn R, Pietrusiewicz J, Siczek A (2012) Effects of soil compaction on root elongation and anatomy of different cereal plant species. Soil and Tillage Research 121, 74–81.
| Effects of soil compaction on root elongation and anatomy of different cereal plant species.Crossref | GoogleScholarGoogle Scholar |
Loh W-Y (2011) Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, 14–23.
| Classification and regression trees.Crossref | GoogleScholarGoogle Scholar |
López-Garrido R, Madejón E, Murillo JM, Moreno F (2011) Soil quality alteration by mouldboard ploughing in a commercial farm devoted to no-tillage under Mediterranean conditions. Agriculture, Ecosystems & Environment 140, 182–190.
| Soil quality alteration by mouldboard ploughing in a commercial farm devoted to no-tillage under Mediterranean conditions.Crossref | GoogleScholarGoogle Scholar |
Machado DFT, Silva SHG, Curi N, Menezes MDd (2019) Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment. Scientia Agricola 76, 243–254.
| Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment.Crossref | GoogleScholarGoogle Scholar |
Mathieu JA, Aires F (2018) Assessment of the agro-climatic indices to improve crop yield forecasting. Agricultural and Forest Meteorology 253–254, 15–30.
| Assessment of the agro-climatic indices to improve crop yield forecasting.Crossref | GoogleScholarGoogle Scholar |
Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing 39, 2784–2817.
| Implementation of machine-learning classification in remote sensing: an applied review.Crossref | GoogleScholarGoogle Scholar |
Maya Gopal PS, Bhargavi R (2019) Performance evaluation of best feature subsets for crop yield prediction using machine learning algorithms. Applied Artificial Intelligence 33, 621–642.
| Performance evaluation of best feature subsets for crop yield prediction using machine learning algorithms.Crossref | GoogleScholarGoogle Scholar |
Melero S, Panettieri M, Madejón E, Macpherson HG, Moreno F, Murillo JM (2011) Implementation of chiselling and mouldboard ploughing in soil after 8 years of no-till management in SW, Spain: effect on soil quality. Soil and Tillage Research 112, 107–113.
| Implementation of chiselling and mouldboard ploughing in soil after 8 years of no-till management in SW, Spain: effect on soil quality.Crossref | GoogleScholarGoogle Scholar |
Melland AR, Antille DL, Dang YP (2017) Effects of strategic tillage on short-term erosion, nutrient loss in runoff and greenhouse gas emissions. Soil Research 55, 201–214.
| Effects of strategic tillage on short-term erosion, nutrient loss in runoff and greenhouse gas emissions.Crossref | GoogleScholarGoogle Scholar |
Mishra S, Mishra D, Santra GH (2016) Applications of machine learning techniques in agricultural crop production: a review paper. Indian Journal of Science and Technology 9, 1–14.
| Applications of machine learning techniques in agricultural crop production: a review paper.Crossref | GoogleScholarGoogle Scholar |
Moreira SG (2019) Desafios para a sustentabilidade dos sistemas de produção com culturas anuais. Informações Agronômicas 4, 1–12.
Nascimento Vd, Arf O, Tarsitano MAA, Garcia NFS, Penteado MdS, Sabundjian MT (2016) Economic analysis of rice and common bean production in succession to green manure crops and mecahnical soil decompression in Brazilian Savannah. Revista Ceres 63, 315–322.
| Economic analysis of rice and common bean production in succession to green manure crops and mecahnical soil decompression in Brazilian Savannah.Crossref | GoogleScholarGoogle Scholar |
Nawaz MF, Bourrié G, Trolard F (2013) Soil compaction impact and modelling. A review. Agronomy for Sustainable Development 33, 291–309.
| Soil compaction impact and modelling. A review.Crossref | GoogleScholarGoogle Scholar |
Nunes MR, Pauletto EA, Denardin JE, Faganello A, Spinelli Pinto LF, Scheunemann T (2014) Persistência dos efeitos da escarificação sobre a compactação de Nitossolo sob plantio direto em região subtropical úmida. Pesquisa Agropecuária Brasileira 49, 531–539.
| Persistência dos efeitos da escarificação sobre a compactação de Nitossolo sob plantio direto em região subtropical úmida.Crossref | GoogleScholarGoogle Scholar |
Nunes MR, Denardin JE, Pauletto EA, Faganello A, Pinto LFS (2015a) Mitigation of clayey soil compaction managed under no-tillage. Soil and Tillage Research 148, 119–126.
| Mitigation of clayey soil compaction managed under no-tillage.Crossref | GoogleScholarGoogle Scholar |
Nunes MR, Denardin JE, Pauletto EA, Faganello A, Pinto LFS (2015b) Effect of soil chiseling on soil structure and root growth for a clayey soil under no-tillage. Geoderma 259–260, 149–155.
| Effect of soil chiseling on soil structure and root growth for a clayey soil under no-tillage.Crossref | GoogleScholarGoogle Scholar |
Pal M (2005) Random forest classifier for remote sensing classification. International Journal of Remote Sensing 26, 217–222.
| Random forest classifier for remote sensing classification.Crossref | GoogleScholarGoogle Scholar |
Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture 121, 57–65.
| Wheat yield prediction using machine learning and advanced sensing techniques.Crossref | GoogleScholarGoogle Scholar |
Peixoto DS, Silva BM, Godinho Silva SH, Karlen DL, Moreira SG, Pereira da Silva AA, Vilela de Resende Á, Norton LD, Curi N (2019a) Diagnosing, ameliorating, and monitoring soil compaction in no-till Brazilian soils. Agrosystems, Geosciences & Environment 2, 1–14.
| Diagnosing, ameliorating, and monitoring soil compaction in no-till Brazilian soils.Crossref | GoogleScholarGoogle Scholar |
Peixoto DS, Silva BM, Oliveira GCd, Moreira SG, da Silva F, Curi N (2019b) A soil compaction diagnosis method for occasional tillage recommendation under continuous no tillage system in Brazil. Soil and Tillage Research 194, 104307
| A soil compaction diagnosis method for occasional tillage recommendation under continuous no tillage system in Brazil.Crossref | GoogleScholarGoogle Scholar |
Peixoto DS, Silva LdCMd, Melo LBBd, Azevedo RP, Araújo BCL, Carvalho TSd, Moreira SG, Curi N, Silva BM (2020) Occasional tillage in no-tillage systems: a global meta-analysis. Science of the Total Environment 745, 140887
| Occasional tillage in no-tillage systems: a global meta-analysis.Crossref | GoogleScholarGoogle Scholar |
Quincke JA, Wortmann CS, Mamo M, Franti T, Drijber RA, García JP (2007) One-time tillage of no-till systems: soil physical properties, phosphorus runoff, and crop yield. Agronomy Journal 99, 1104–1110.
| One-time tillage of no-till systems: soil physical properties, phosphorus runoff, and crop yield.Crossref | GoogleScholarGoogle Scholar |
Rabot E, Wiesmeier M, Schlüter S, Vogel H-J (2018) Soil structure as an indicator of soil functions: a review. Geoderma 314, 122–137.
| Soil structure as an indicator of soil functions: a review.Crossref | GoogleScholarGoogle Scholar |
R Development Core Team (2019) ‘R: a language and environment for statistical computing’. (R Foundation for Statistical Computing: Viena, Austria) Available at https://www.rproject.org
Reichert JM, Suzuki LEAS, Reinert DJ, Horn R, Håkansson I (2009) Reference bulk density and critical degree-of-compactness for no-till crop production in subtropical highly weathered soils. Soil and Tillage Research 102, 242–254.
| Reference bulk density and critical degree-of-compactness for no-till crop production in subtropical highly weathered soils.Crossref | GoogleScholarGoogle Scholar |
Reynolds WD, Bowman BT, Drury CF, Tan CS, Lu X (2002) Indicators of good soil physical quality: density and storage parameters. Geoderma 110, 131–146.
| Indicators of good soil physical quality: density and storage parameters.Crossref | GoogleScholarGoogle Scholar |
Reynolds WD, Drury CF, Yang XM, Tan CS (2008) Optimal soil physical quality inferred through structural regression and parameter interactions. Geoderma 146, 466–474.
| Optimal soil physical quality inferred through structural regression and parameter interactions.Crossref | GoogleScholarGoogle Scholar |
Reynolds WD, Drury CF, Tan CS, Fox CA, Yang XM (2009) Use of indicators and pore volume-function characteristics to quantify soil physical quality. Geoderma 152, 252–263.
| Use of indicators and pore volume-function characteristics to quantify soil physical quality.Crossref | GoogleScholarGoogle Scholar |
Richetti J, Judge J, Boote KJ, Johann JA, Uribe-Opazo MA, Becker WR, Paludo A, Silva LCdA (2018) Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil. Journal of Applied Remote Sensing 12, 026029
| Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil.Crossref | GoogleScholarGoogle Scholar |
Sá JCdM (1999) Manejo da fertilidade do solo no Sistema Plantio Direto. In ‘Inter-relações fertilidade, biologia do solo e nutrição de plantas’. (Eds JO Siqueira, FMS Moreira, AS Lopes, LRG Guilherme, V Faquim, AE Furtini Neto) pp. 267–319. (Sociedade Brasileira de Ciência do Solo: Lavras, MG)
Santos HGd, Jacomine PKT, Anjos LHCd, Oliveira VÁd, Lumbreras JF, Coelho MR, Almeida JAd, Araújo Filho JCd, Oliveira JBd, Cunha TJF (2018) ‘Sistema brasileiro de classificação de solos’. (Embrapa: Brasília, DF)
Saradhi VV, Karnik H, Mitra P (2005) A decomposition method for support vector clustering. In ‘Proceedings of 2005 international conference on intelligent sensing and information processing’. pp. 268–271. (IEEE) 10.1109/ICISIP.2005.1529460
Silva BM, Oliveira GC, Serafim ME, Silva ÉA, Ferreira MM, Norton LD, Curi N (2015) Critical soil moisture range for a coffee crop in an oxidic latosol as affected by soil management. Soil and Tillage Research 154, 103–113.
| Critical soil moisture range for a coffee crop in an oxidic latosol as affected by soil management.Crossref | GoogleScholarGoogle Scholar |
Smidt ER, Conley SP, Zhu J, Arriaga FJ (2016) Identifying field attributes that predict soybean yield using random forest analysis. Agronomy Journal 108, 637–646.
| Identifying field attributes that predict soybean yield using random forest analysis.Crossref | GoogleScholarGoogle Scholar |
Soil Science Glossary Terms Committee (2008) ‘Glossary of soil science terms’. (Soil Science Society of America: Madison, WI)
Soil Survey Staff (2014) ‘Keys to soil taxonomy’. (USDA: Washington, DC)
Szatanik-Kloc A, Horn R, Lipiec J, Siczek A, Szerement J (2018) Soil compaction-induced changes of physicochemical properties of cereal roots. Soil and Tillage Research 175, 226–233.
| Soil compaction-induced changes of physicochemical properties of cereal roots.Crossref | GoogleScholarGoogle Scholar |
Tulbure MG, Wimberly MC, Boe A, Owens VN (2012) Climatic and genetic controls of yields of switchgrass, a model bioenergy species. Agriculture, Ecosystems & Environment 146, 121–129.
| Climatic and genetic controls of yields of switchgrass, a model bioenergy species.Crossref | GoogleScholarGoogle Scholar |
van Raij B, Cantarella H, Quaggio JA, Furlani AMC (1997) ‘Recomendações de adubação e calagem para o Estado de São Paulo’. (IAC: Campinas, SP)