A Bayesian approach to assessing uncertainty in the effect of fertilization strategies on paddy rice yield via multiple on-farm experiments

References

• Achim, A., Bezerianos, A., & Tsakalides, P. (2001). Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Transactions on Medical Imaging, 20(8), 772–783. https://doi.org/10.1109/42.938245
   PubMed Web of Science ®Google Scholar
• Alesso, C. A., Cipriotti, P. A., Bollero, G. A., & Martin, N. F. (2021). Design of on-farm precision experiments to estimate site-specific crop responses. Agronomy Journal, 113(2), 1366–1380. https://doi.org/10.1002/agj2.20572
   Web of Science ®Google Scholar
• Basak, M. N., Sen, S. K., & Bhattacharjee, P. K. (1962). Effects of high nitrogen fertilization and lodging on rice yield 1. Agronomy Journal, 54(6), 477–480. https://doi.org/10.2134/agronj1962.00021962005400060003x
   Web of Science ®Google Scholar
• Besag, J., & Higdon, D. (1999). Bayesian analysis of agricultural field experiments. Journal of the Royal Statistical Society Series B: Statistical Methodology, 61(4), 691–746. https://doi.org/10.1111/1467-9868.00201
   Google Scholar
• Bijay-Singh, & Craswell, E. (2021). Fertilizers and nitrate pollution of surface and ground water: An increasingly pervasive global problem. SN Applied Sciences, 3(4), 518. https://doi.org/10.1007/s42452-021-04521-8
   Web of Science ®Google Scholar
• Carl, G., & Kühn, I. (2007). Analyzing spatial autocorrelation in species distributions using Gaussian and logit models. Ecological Modelling, 207(2–4), 159–170. https://doi.org/10.1016/j.ecolmodel.2007.04.024
   Web of Science ®Google Scholar
• Choy, S. L., O’Leary, R., & Mengersen, K. (2009). Elicitation by design in ecology: Using expert opinion to inform priors for Bayesian statistical models. Ecology, 90(1), 265–277. https://doi.org/10.1890/07-1886.1
   PubMed Web of Science ®Google Scholar
• Cook, S., Lacoste, M., Evans, F., Ridout, M., Gibberd, M., & Oberthür, T. (2018). An on-farm experimental philosophy for farmer-centric digital innovation. Proceedings of 14th International Conference on Precision Agriculture, International Society of Precision Agriculture, Montreal, CA.
   Google Scholar
• Dendoncker, N., Rounsevell, M., & Bogaert, P. (2007). Spatial analysis and modelling of land use distributions in Belgium. Computers, Environment and Urban Systems, 31(2), 188–205. https://doi.org/10.1016/j.compenvurbsys.2006.06.004
   Web of Science ®Google Scholar
• Donald, M., Alston, C. L., Young, R. R., & Mengersen, K. L. (2011). A Bayesian analysis of an agricultural field trial with three spatial dimensions. Computational Statistics & Data Analysis, 55(12), 3320–3332. https://doi.org/10.1016/j.csda.2011.06.022
   Web of Science ®Google Scholar
• Ellison, A. M. (1996). An introduction to bayesian inference for ecological research and environmental decision‐making. Ecological Applications, 6(4), 1036–1046. https://doi.org/10.2307/2269588
   Web of Science ®Google Scholar
• Eltaib, S. M., Soom, M., Amin, M., Hanafi, M. M., Rashid, A., Shariff, M., & Wayayok, A. (2002). Spatial variability of N, P, and K in rice field in Sawah Sempadan, Malaysia. Songklanakarin Journal of Science and Technology, 24(2), 322–328.
   Google Scholar
• Evans, F. H., Salas, A. R., Rakshit, S., Scanlan, C. A., & Cook, S. E. (2020). Assessment of the use of geographically weighted regression for analysis of large on-farm experiments and implications for practical application. Agronomy, 10(11), 1720. https://doi.org/10.3390/agronomy10111720
   Google Scholar
• Gao, Y., Yu, G., Luo, C., Zhou, P., & Cannon, A. J. (2012). Groundwater nitrogen pollution and assessment of its health risks: A case study of a typical village in rural-urban continuum, China. Public Library of Science ONE, 7(4), e33982. https://doi.org/10.1371/journal.pone.0033982
   Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. ‎Chapman and Hall/CRC. https://doi.org/10.1201/b16018
   Google Scholar
• Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472. https://doi.org/10.1214/ss/1177011136
   Google Scholar
• Hegedus, P. B., Maxwell, B., Sheppard, J., Loewen, S., Duff, H., Morales-Luna, G., & Peerlinck, A. (2023). Towards a low-cost comprehensive process for on-farm precision experimentation and analysis. Agriculture, 13(3), 524. https://doi.org/10.3390/agriculture13030524
   Google Scholar
• Hegedus, P. B., Maxwell, B. D., & Mieno, T. (2023). Assessing performance of empirical models for forecasting crop responses to variable fertilizer rates using on-farm precision experimentation. Precision Agriculture, 24(2), 677–704. https://doi.org/10.1007/s11119-022-09968-2
   Web of Science ®Google Scholar
• Hellegers, P. (2022). Food security vulnerability due to trade dependencies on Russia and Ukraine. Food Security, 14(6), 1503–1510. https://doi.org/10.1007/s12571-022-01306-8
   PubMed Web of Science ®Google Scholar
• Hooten, M. B., & Hobbs, N. T. (2015). A guide to Bayesian model selection for ecologists. Ecological Monographs, 85(1), 3–28. https://doi.org/10.1890/14-0661.1
   Web of Science ®Google Scholar
• Istiak Md, A., Syeed, M. M. M., Hossain, M. S., Uddin, M. F., Hasan, M., Khan, R. H., & Azad, N. S. (2023). Adoption of unmanned aerial vehicle (UAV) imagery in agricultural management: A systematic literature review. Ecological Informatics, 78, 102305. https://doi.org/10.1016/j.ecoinf.2023.102305
   Web of Science ®Google Scholar
• Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of artificial intelligence in agriculture sector. Advanced Agrochemical, 2(1), 15–30. https://doi.org/10.1016/j.aac.2022.10.001
   Google Scholar
• Ju, X. T., Kou, C. L., Zhang, F. S., & Christie, P. (2006). Nitrogen balance and groundwater nitrate contamination: Comparison among three intensive cropping systems on the North China plain. Environmental Pollution, 143(1), 117–125. https://doi.org/10.1016/j.envpol.2005.11.005
   PubMed Web of Science ®Google Scholar
• Katzfuss, M., Guinness, J., Gong, W., & Zilber, D. (2020). Vecchia approximations of gaussian-process predictions. Journal of Agricultural, Biological and Environmental Statistics, 25(3), 383–414. https://doi.org/10.1007/s13253-020-00401-7
   Google Scholar
• Koneswaran, G., & Nierenberg, D. (2008). Global farm animal production and global warming: Impacting and mitigating climate change. Environmental Health Perspectives, 116(5), 578–582. https://doi.org/10.1289/ehp.11034
   PubMed Web of Science ®Google Scholar
• Kyveryga, P. M., Mueller, T. A., & Mueller, D. S. (2018). On-farm replicated strip trials. In D. K. Shannon, D. E. Clay, & N. R. Kitchen (Eds.), Precision agriculture basics (pp. 189–208). American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. https://doi.org/10.2134/precisionagbasics.2016.0096
   Google Scholar
• Lacoste, M., Cook, S., McNee, M., Gale, D., Ingram, J., Bellon-Maurel, V., MacMillan, T., Sylvester-Bradley, R., Kindred, D., Bramley, R., Tremblay, N., Longchamps, L., Thompson, L., Ruiz, J., García, F. O., Maxwell, B., Griffin, T., Oberthür, T., Huyghe, C., & Hall, A. (2022). On-farm experimentation to transform global agriculture. Nature Food, 3(1), 11–18. https://doi.org/10.1038/s43016-021-00424-4
   PubMedGoogle Scholar
• Li, X., Mieno, T., & Bullock, D. S. (2023). The economic performances of different trial designs in on-farm precision experimentation: A monte carlo evaluation. Precision Agriculture, 24(6), 2500–2521. https://doi.org/10.1007/s11119-023-10050-8
   Web of Science ®Google Scholar
• Long, D. H., Lee, F. N., & TeBeest, D. O. (2000). Effect of nitrogen fertilization on disease progress of rice blast on susceptible and resistant cultivars. Plant Disease, 84(4), 403–409. https://doi.org/10.1094/PDIS.2000.84.4.403
   PubMed Web of Science ®Google Scholar
• Marchant, B., Rudolph, S., Roques, S., Kindred, D., Gillingham, V., Welham, S., Coleman, C., & Sylvester-Bradley, R. (2019). Establishing the precision and robustness of farmers’ crop experiments. Field Crops Research, 230, 31–45. https://doi.org/10.1016/j.fcr.2018.10.006
   Web of Science ®Google Scholar
• McElreath, R. (2018). Statistical rethinking. Chapman and Hall/CRC. https://doi.org/10.1201/9781315372495
   Google Scholar
• Mia, M. S., Tanabe, R., Habibi, L. N., Hashimoto, N., Homma, K., Maki, M., Matsui, T., & Tanaka, T. S. T. (2023). Multimodal deep learning for rice yield prediction using UAV-Based multispectral imagery and weather data. Remote Sensing, 15(10), 2511. https://doi.org/10.3390/rs15102511
   Web of Science ®Google Scholar
• Ministry of Agriculture, Forestry and Fisheries. (2021, May 12). Strategy for sustainable food systems, MIDORI. https://www.maff.go.jp/e/policies/env/env_policy/meadri.html
   Google Scholar
• Morrison, K. T., Nelson, T. A., Nathoo, F. S., & Ostry, A. S. (2012). Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency. International Journal of Geographical Information Science, 26(7), 1213–1229. https://doi.org/10.1080/13658816.2011.633491
   Web of Science ®Google Scholar
• Natanegara, F., Neuenschwander, B., Seaman, J. W., Kinnersley, N., Heilmann, C. R., Ohlssen, D., & Rochester, G. (2014). The current state of Bayesian methods in medical product development: Survey results and recommendations from the DIA Bayesian scientific working group. Pharmaceutical Statistics, 13(1), 3–12. https://doi.org/10.1002/pst.1595
   PubMed Web of Science ®Google Scholar
• Paccioretti, P., Bruno, C., Gianinni Kurina, F., Córdoba, M., Bullock, D. S., & Balzarini, M. (2021). Statistical models of yield in on‐farm precision experimentation. Agronomy Journal, 113(6), 4916–4929. https://doi.org/10.1002/agj2.20833
   Web of Science ®Google Scholar
• Piepho, H. P., Richter, C., Spilke, J., Hartung, K., Kunick, A., & Thöle, H. (2011). Statistical aspects of on-farm experimentation. Crop and Pasture Science, 62(9), 721–735. https://doi.org/10.1071/CP11175
   Web of Science ®Google Scholar
• R Core Team. (2024, May 20). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
   Google Scholar
• Rabbi, M. F., Ben Hassen, T., El Bilali, H., Raheem, D., & Raposo, A. (2023). Food security challenges in Europe in the context of the prolonged Russian–Ukrainian conflict. Sustainability, 15(6), 4745. https://doi.org/10.3390/su15064745
   Web of Science ®Google Scholar
• Randive, K., Raut, T., & Jawadand, S. (2021). An overview of the global fertilizer trends and India’s position in 2020. Mineral Economics, 34(3), 371–384. https://doi.org/10.1007/s13563-020-00246-z
   Web of Science ®Google Scholar
• Roques, S. E., Kindred, D. R., Berry, P., & Helliwell, J. (2022). Successful approaches for on-farm experimentation. Field Crops Research, 287, 108651. https://doi.org/10.1016/j.fcr.2022.108651
   Web of Science ®Google Scholar
• Sadeghi, A., Graff, C. D., Starr, J., McCarty, G., Codling, E., & Sefton, K. (2006). Spatial variability of soil phosphorus levels before and after poultry litter application. Soil Science, 171(11), 850–857. https://doi.org/10.1097/01.ss.0000228060.46205.7b
   Web of Science ®Google Scholar
• Shirley, R., Pope, E., Bartlett, M., Oliver, S., Quadrianto, N., Hurley, P., Duivenvoorden, S., Rooney, P., Barrett, A. B., Kent, C., & Bacon, J. (2020). An empirical, Bayesian approach to modelling crop yield: Maize in USA. Environmental Research Communications, 2(2), 025002. https://doi.org/10.1088/2515-7620/ab67f0
   Google Scholar
• Spiegelhalter, D. J., Myles, J. P., Jones, D. R., & Abrams, K. R. (1999). Methods in health service research: An introduction to bayesian methods in health technology assessment. BMJ: British Medical Journal, 319(7208), 508–512. https://doi.org/10.1136/bmj.319.7208.508
   Google Scholar
• Stan Development Team. (2022). Stan user’s guide (version 2.33). https://mc-stan.org/docs/2_33/stan-users-guide-2_33.pdf
   Google Scholar
• Stefanova, K. T., Brown, J., Grose, A., Cao, Z., Chen, K., Gibberd, M., & Rakshit, S. (2023). Statistical analysis of comparative experiments based on large strip on-farm trials. Field Crops Research, 297, 108945. https://doi.org/10.1016/j.fcr.2023.108945
   Web of Science ®Google Scholar
• Sutton, A. J., & Abrams, K. R. (2001). Bayesian methods in meta-analysis and evidence synthesis. Statistical Methods in Medical Research, 10(4), 277–303. https://doi.org/10.1177/096228020101000404
   PubMed Web of Science ®Google Scholar
• Tanaka, T. S. T. (2021). Assessment of design and analysis frameworks for on-farm experimentation through a simulation study of wheat yield in Japan. Precision Agriculture, 22(5), 1601–1616. https://doi.org/10.1007/s11119-021-09802-1
   Web of Science ®Google Scholar
• Tanaka, T. S. T., Mieno, T., Tanabe, R., Matsui, T., & Bullock, D. S. (2023). Toward an effective approach for on-farm experimentation: Lessons learned from a case study of fertilizer application optimization in Japan. Precision Agriculture, 24(5), 2044–2060. https://doi.org/10.1007/s11119-023-10029-5
   Web of Science ®Google Scholar
• Tian, H., Lu, C., Melillo, J., Ren, W., Huang, Y., Xu, X., Liu, M., Zhang, C., Chen, G., Pan, S., Liu, J., & Reilly, J. (2012). Food benefit and climate warming potential of nitrogen fertilizer uses in China. Environmental Research Letters, 7(4), 044020. https://doi.org/10.1088/1748-9326/7/4/044020
   Web of Science ®Google Scholar
• Wang, J. C., Holan, S. H., Nandram, B., Barboza, W., Toto, C., & Anderson, E. (2012). A Bayesian approach to estimating agricultural yield based on multiple repeated surveys. Journal of Agricultural, Biological, and Environmental Statistics, 17(1), 84–106. https://doi.org/10.1007/s13253-011-0067-5
   Web of Science ®Google Scholar
• Weerahewa, J., & Dayananda, D. (2023). Land use changes and economic effects of alternative fertilizer policies: A simulation analysis with a bio-economic model for a tank village of Sri Lanka. Agricultural Systems, 205, 103563. https://doi.org/10.1016/j.agsy.2022.103563
   Web of Science ®Google Scholar
• Zhang, W. L., Tian, Z. X., Zhang, N., & Li, X. Q. (1996). Nitrate pollution of groundwater in northern China. Agriculture, Ecosystems & Environment, 59(3), 223–231. https://doi.org/10.1016/0167-8809(96)01052-3
   Web of Science ®Google Scholar