Untangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random forest

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cg.authorship.typesCGIAR and developing country institute
cg.contributor.affiliationOdisha University of Agriculture and Technology
cg.contributor.affiliationIndian Council of Agricultural Research
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.contributor.affiliationBidhan Chandra Krishi Viswavidyalaya
cg.contributor.donorIndian Council of Agricultural Research
cg.contributor.donorCGIAR Trust Fund
cg.contributor.initiativeTransforming Agrifood Systems in South Asia
cg.creator.identifierTek Sapkota: 0000-0001-5311-0586
cg.howPublishedFormally Published
cg.identifier.doihttps://doi.org/10.1016/j.compag.2022.106965
cg.isijournalISI Journal
cg.issn1872-7107
cg.journalComputers and Electronics in Agriculture
cg.number106965
cg.reviewStatusPeer Review
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.actionAreaSystems Transformation
cg.subject.impactAreaNutrition, health and food security
cg.subject.impactAreaClimate adaptation and mitigation
cg.volume197
dc.contributor.authorGarnaik, Saheed
dc.contributor.authorSamant, Prasanna Kumar
dc.contributor.authorMandal, Mitali
dc.contributor.authorMohanty, Tushar Ranjan
dc.contributor.authorDwibedi, Sanat Kumar
dc.contributor.authorPatra, Ranjan Kumar
dc.contributor.authorMohapatra, Kiran Kumar
dc.contributor.authorWanjari, Ravi H.
dc.contributor.authorSethi, Debadatta
dc.contributor.authorSena, Dipaka Ranjan
dc.contributor.authorSapkota, Tek Bahadur
dc.contributor.authorNayak, Jagmohan
dc.contributor.authorPatra, Sridhar
dc.contributor.authorParihar, Chiter Mal
dc.contributor.authorNayak, Harisankar
dc.date.accessioned2023-02-02T11:10:40Zen
dc.date.available2023-02-02T11:10:40Zen
dc.identifier.urihttps://hdl.handle.net/10568/128392
dc.titleUntangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random foresten
dcterms.abstractIn a 16-years long-term fertilizer experiment, an in-depth study was carried out to evaluate the changes in soil physical, chemical, and biological properties under long-term fertilizer application and establish cause and effect relationship between soil properties and rice productivity using interpretable machine learning. There were 12 treatments involving control (without fertilizer application), 100% N (recommended dose of nitrogen), 100% NP (recommended dose of nitrogen and phosphorus), 100% PK (recommended dose of phosphorus and potassium), 100% NPK (recommended dose of nitrogen, phosphorus, and potassium), 150% NPK (50% higher nitrogen, phosphorus, and potassium than recommended), 100% NPK + Zn (recommended nitrogen, phosphorus, and potassium along with Zinc), 100% NPK + FYM (recommended nitrogen, phosphorus, and potassium along with farmyard manure (FYM)), 100% NPK + FYM + LIME (recommended nitrogen, phosphorus, and potassium along with FYM and lime), 100% NPK + Zn + S (recommended nitrogen, phosphorus, and potassium along with zinc and sulphur), 100% NPK + Zn + B (recommended nitrogen, phosphorus, and potassium along with Zinc and Boron) and 100% NPK + Lime (recommended nitrogen, phosphorus, and potassium along with lime). At first, a conditional random forest model was built, based on which important variables were selected using the permutation-based variable importance approach. Further, the accumulated local effect plot was used to establish a cause and effect relationship between important soil properties and rice yield. Although most of the soil properties varied across the treatments, total potassium, protease, urease, and permanganate oxidisable carbon are the most important soil properties, individually accounting for up to 400 kg ha−1 variation in the rice productivity. The study demonstrated how interpretable machine learning techniques could be used in long-term fertilizer experiments to unravel the most meaningful information, and these techniques can be used in other similar long-term experiments.en
dcterms.accessRightsLimited Access
dcterms.audienceScientists
dcterms.bibliographicCitationGarnaik, S., Samant, P.K., Mandal, M., Mohanty, T.R., Dwibedi, S.K., Patra, R.K., Mohapatra, K.K., Wanjari, R.H., Sethi, D., Sena, D.R., Sapkota, T.B., Nayak, J., Patra, S., Parihar, C. M. and Nayak, H.S. 2022. Untangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random forest. Computers and Electronics in Agriculture 197:106965.en
dcterms.issued2022-06
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherElsevier
dcterms.subjectmachine learningen
dcterms.subjectparcelsen
dcterms.subjectsoil propertiesen
dcterms.subjectriceen
dcterms.subjectfertilizersen
dcterms.typeJournal Article

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