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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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Title Untangling the effect of soil quality on rice productivity under a 16-years long-term fertilizer experiment using conditional random forest
 
Creator Garnaik, Saheed
Samant, Prasanna Kumar
Mandal, Mitali
Mohanty, Tushar Ranjan
Dwibedi, Sanat Kumar
Patra, Ranjan Kumar
Mohapatra, Kiran Kumar
Wanjari, Ravi H.
Sethi, Debadatta
Sena, Dipaka Ranjan
Sapkota, Tek Bahadur
Nayak, Jagmohan
Patra, Sridhar
Parihar, Chiter Mal
Nayak, Harisankar
 
Subject machine learning
parcels
soil properties
rice
fertilizers
 
Description In 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.
 
Date 2022-06-15
2023-02-02T11:10:40Z
2023-02-02T11:10:40Z
 
Type Journal Article
 
Identifier Garnaik, 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.
1872-7107
https://hdl.handle.net/10568/128392
https://doi.org/10.1016/j.compag.2022.106965
 
Language en
 
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Source Computers and Electronics in Agriculture