This study was carried out in the first-order inland valleys of Pronou, Gbêkê region, central C？te d’Ivoire. It investigated the agronomic potential of these ecosystems for optimized rice production under conditions of interannual climatic variability. For the first time in this production system, a prospective modeling approach was adopted using the APSIMX R package interface, within which crop yields were simulated via a process-informed empirical model calibrated against published dose-response relationships for improved inland valley rice varieties in West Africa. This integrative framework combined soil physico-chemical, hydrological, and climatic data (2020-2023) with three rice varieties: NERICA-L19, WAB 56-50, and a local ecotype, simulated under six nitrogen application rates (0, 40, 80, 120, 160, 200 kg N/ha) across three topographic positions. Results demonstrated consistent agronomic stability of the studied inland valleys (feasibility indices ≥ 0.80) despite interannual climatic fluctuations. The optimal planting window spanned April to June, with yield potential peaking in May for all tested varieties. Among the tested varieties, NERICA-L19 consistently outperformed WAB 56-50 and the local ecotype, with predicted yields reaching 6860 kg/ha at 200 kg N/ha and a nitrogen use efficiency exceeding that of the local variety by 18%. Nitrogen fertilization exerted significant and dose-dependent effects on grain yield, predominantly during the climatically favorable period (p < 0.01). A multiple linear regression model (R2 = 0.87; F = 62.4; p < 0.001) corroborated the superiority of an integrated management strategy combining optimized variety selection, sowing date adjustment, and calibrated nitrogen fertilization. At the recommended rate of 200 kg N/ha, net economic benefits reached 2,174,000 FCFA/ha for NERICA-L19, with a negligible risk of economic loss across all simulated price and yield scenarios. These results provide a robust, evidence-based framework for adaptive nitrogen management in first-order inland valley rice systems, with direct implications for enhancing the resilience and sustainability of smallholder rice production under increasing climatic variability in West Africa.
References
[1]
Islam, M.R. (2021) Crop Calendar Mapping of Bangladesh Rice Paddy Field with ALOS-2 Scansar Data. AdvancesinRemoteSensing, 10, 115-129. https://doi.org/10.4236/ars.2021.103008
[2]
OECD-FAO (2024) Agricultural Outlook 2024-2033. OECD Publishing.
[3]
Yéo, Y.S., Kouadio, K.S.A., Elliott, D., Ouedraogo, M. and Singh, M. (2024) RIICE Development in Cote d’Ivoire: Africa Rice Report of 2024. Transforming Agrifood Systems in West and Central Africa Initiative (TAFS-WCA), 17 p. https://alliancebioversityciat.org/publications-data/riice-development-cote-divoire-africarice-report-2024-transforming-agrifood
[4]
Kotchi, K.J., Kouayé, O.D., Kanga, K.M.J., N’guessan, K.G. and Konan, K.P. (2023) La transformation du riz paddy, un défi majeur pour le développement durable de la riziculture dans la région du G？h (C？te d’Ivoire). Revue Ivoirienne de Géographie des Savanes, 15, 87-102.
[5]
Dossou-Yovo, E.R., Kouadio, S.A.K. and Saito, K. (2023) Effects of Mid-Season Drainage on Iron Toxicity, Rice Yield, and Water Productivity in Irrigated Systems in the Derived Savannah Agroecological Zone of West Africa. Field Crops Research, 296, Article 108901. https://doi.org/10.1016/j.fcr.2023.108901
[6]
Affholder, F., Poeydebat, C., Corbeels, M., Scopel, E. and Tittonell, P. (2013) The Yield Gap of Major Food Crops in Family Agriculture in the Tropics: Assessment and Analysis through Field Surveys and Modelling. Field Crops Research, 143, 106-118. https://doi.org/10.1016/j.fcr.2012.10.021
[7]
N’guessan, K.G., Kotchi, K.J., Gagne, F.A. and Kouame, A.H. (2021) Les bas-fonds, un potentiel rizicole sous-exploité dans la sous-préfecture de Daloa. Revue Ivoir-ienne de Géographie, 23, 71-86.
[8]
Manneh, B., Kiepe, P., Sie, M., Ndjiondjop, M., Drame, N. and Traore, K. (2007) Exploiting Partnerships in Research and Development to Help African Rice Farmers Cope with Climate Variability. Journal of SAT Agricultural Research/SAT eJournal, 4, 1-24. https://www.researchgate.net/publication/228692046_Exploiting_partnerships_in_research_and_development_to_help_African_rice_farmers_cope_with_climate_variability
[9]
Saito, K., Sokei, Y. and Wopereis, M.C.S. (2012) Enhancing Rice Productivity in West Africa through Genetic Improvement. CropScience, 52, 484-493. https://doi.org/10.2135/cropsci2010.12.0734
[10]
Somado, E.A., Guei, R.G. and Keya, S.O. (2008) NERICA: The New Rice for Africa-A Compendium. Africa Rice Center, 210 p.
[11]
Pansu, M. and Gautheyrou, J. (2006) Handbook of Soil Analysis: Mineralogical, Organic and Inorganic Methods. Springer-Verlag.
Al-Shamaa, K., Crimi, M., Kehel, Z. and Aparicio, J. (2025) QBMS: Query the Breeding Management System(s). R Package Version 2.0.0. https://CRAN.R-project.org/package=QBMS
[14]
Wilby, R.L. and Dawson, C.W. (2012) The Statistical Downscaling Model: Insights from One Decade of Application. International Journal of Climatology, 33, 1707-1719. https://doi.org/10.1002/joc.3544
[15]
Roudier, P., Sultan, B., Quirion, P. and Berg, A. (2011) The Impact of Future Climate Change on West African Crop Yields: What Does the Recent Literature Say? Global Environmental Change, 21, 1073-1083. https://doi.org/10.1016/j.gloenvcha.2011.04.007
[16]
van Wart, J., van Bussel, L.G.J., Wolf, J., Licker, R., Grassini, P., Nelson, A., et al. (2013) Use of Agro-Climatic Zones to Upscale Simulated Crop Yield Potential. Field Crops Research, 143, 44-55. https://doi.org/10.1016/j.fcr.2012.11.023
[17]
Krupnik, T.J., Shennan, C., Settle, W.H., Demont, M., Ndiaye, A.B. & Rodenburg, J. (2012) Improving Irrigated Rice Production in the Senegal River Valley through Experiential Learning and Innovation. Agricultural Systems, 109, 101-112. http://dx.doi.org/10.1016/j.agsy.2012.01.008
[18]
van Oort, P.A.J., Saito, K., Tanaka, A., Amovin-Assagba, E., Van Bussel, L.G.J., van Wart, J., et al. (2015) Assessment of Rice Self-Sufficiency in 2025 in Eight African Countries. Global Food Security, 5, 39-49. https://doi.org/10.1016/j.gfs.2015.01.002
[19]
Saito, K., Nelson, A., Zwart, S.J., Niang, A., Sow, A., Yoshida, H., et al. (2013) Towards a Better Understanding of Biophysical Determinants of Yield Gaps and the Potential for Expansion of the Rice Area in Africa. In: Wopereis, M.C.S., et al., Eds., Realizing Africa’s Rice Promise, CABI, 188-203. https://doi.org/10.1079/9781845938123.0188
[20]
Tanaka, A., Johnson, J., Senthilkumar, K., Akakpo, C., Segda, Z., Yameogo, L.P., et al. (2017) On-Farm Rice Yield and Its Association with Biophysical Factors in Sub-Saharan Africa. European Journal of Agronomy, 85, 1-11. https://doi.org/10.1016/j.eja.2016.12.010
[21]
Baudron, F., Misiko, M., Getnet, B., Nazare, R., Sariah, J. and Kaumbutho, P. (2019) A Farm-Level Assessment of Labor and Mechanization in Eastern and Southern Africa. Agronomy for Sustainable Development, 39, Article 17. https://doi.org/10.1007/s13593-019-0563-5
[22]
Diarisso, T., Corbeels, M., Andrieu, N., Djamen, P. and Tittonell, P. (2015) Biomass Transfers and Nutrient Budgets of the Agro-Pastoral Systems in a Village Territory in South-Western Burkina Faso. Nutrient Cycling in Agroecosystems, 101, 295-315. https://doi.org/10.1007/s10705-015-9679-4
[23]
Falconnier, G.N., Descheemaeker, K., Van Mourik, T.A., Adam, M., Sogoba, B. and Giller, K.E. (2017) Co-Learning Cycles to Support the Design of Innovative Farm Systems in Southern Mali. European Journal of Agronomy, 89, 61-74. https://doi.org/10.1016/j.eja.2017.06.008
[24]
Tittonell, P., Scopel, E., Andrieu, N., Posthumus, H., Mapfumo, P., Corbeels, M., et al. (2012) Agroecology-Based Aggradation-Conservation Agriculture (ABACO): Targeting Innovations to Combat Soil Degradation and Food Insecurity in Semi-Arid Africa. Field Crops Research, 132, 168-174. https://doi.org/10.1016/j.fcr.2011.12.011
[25]
R Core Team (2025) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://cran.r-project.org/doc/manuals/r-release/fullrefman.pdf
[26]
Wickham, H., Fran？ois, R., Henry, L. and Müller, K. (2019) Dplyr: A Grammar of Data Manipulation. R Package Version 1.0.0. https://CRAN.R-project.org/package=dplyr
[27]
Wickham, H. (2016) Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag.
[28]
Wei, T. and Simko, V. (2021) R Package Corrplot: Visualization of a Correlation Matrix. Version 0.92. https://github.com/taiyun/corrplot
[29]
Webster, R. and Oliver, M.A. (2007) Geostatistics for Environmental Scientists. Wiley. https://doi.org/10.1002/9780470517277
[30]
Hazelton, P. and Murphy, B. (2016) Interpreting Soil Test Results: What Do All the Numbers Mean? 3rd Edition, CSIRO Publishing.
[31]
Hazelton, P. and Murphy, B. (2016) Interpreting Soil Test Results: What Do All the Numbers Mean? CSIRO Publishing.
[32]
Touré, A., Becker, M., Johnson, D.E., Koné, B., Kossou, D.K. and Yao, K. (2009) Influence of Hydrological Position on Soil Properties and Rice Yield in Inland Valleys of C？te d’Ivoire. Field Crops Research, 114, 304-310. https://doi.org/10.1016/j.fcr.2009.08.015
[33]
Abe, S.S., Buri, M.M., Issaka, R.N., Kiepe, P. and Wakatsuki, T. (2010) Soil Fertility Potential for Rice Production in West African Lowlands. Japan Agricultural Research Quarterly: JARQ, 44, 343-355. https://doi.org/10.6090/jarq.44.343
[34]
Diagne, A., Amovin-Assagba, E., Futakuchi, K. and Wopereis, M.C.S. (2013) Estimation of Cultivated Area, Number of Farming Households and Yield for Major Rice-Growing Environments in Africa. In: Wopereis, M.C.S., et al., Eds., Realizing Africa’s Rice Promise, CABI, 35-45. https://doi.org/10.1079/9781845938123.0035
[35]
Diagne, A., Glover, S., Groom, B. and Phillips J. (2012) Africa’s Green Revolution? The Determinants of the Adoption of NERICAs in West Africa. SOAS Department of Economics Working Paper Series, No. 174, SOAS, University of London. https://ideas.repec.org/p/soa/wpaper/174.html
[36]
Peng, S., Buresh, R.J., Huang, J., Zhong, X., Zou, Y., Yang, J., et al. (2010) Improving Nitrogen Fertilization in Rice by Sitespecific N Management. Agronomy for Sustainable Development, 30, 649-656. https://doi.org/10.1051/agro/2010002
[37]
Zou, Y., Zhang, Y., Cui, J., Gao, J., Guo, L. and Zhang, Q. (2023) Nitrogen Fertilization Application Strategies Improve Yield of the Rice Cultivars with Different Yield Types by Regulating Phytohormones. Scientific Reports, 13, Article No. 21803. https://doi.org/10.1038/s41598-023-48491-w
[38]
Saito, K., Linquist, B., Keobualapha, B., Phanthaboon, K., Shiraiwa, T. and Horie, T. (2006) Cropping Intensity and Rainfall Effects on Upland Rice Yields in Northern Laos. Plant and Soil, 284, 175-185. https://doi.org/10.1007/s11104-006-0049-5
[39]
FAO (2010) Climate-Smart Agriculture: Policies, Practices and Financing for Food Security, Adaptation and Mitigation. FAO, 41 p.
[40]
Rodenburg, J. and Johnson, D.E. (2009) Weed Management in Rice‐Based Cropping Systems in Africa. Advances in Agronomy, 103, 149-218. https://doi.org/10.1016/s0065-2113(09)03004-1
[41]
Fafchamps, M. (1999) Rural Poverty, Risk and Development. Food and Agriculture Organization. http://digitallibrary.un.org/record/415280
