Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions varies significantly from one farmer to another, making it challenging to accurately estimate crop production through crop models. This limitation has implications for the reliability of using crop models as agricultural decision-making support tools. To support decision making in agriculture, an approach combining a genetic algorithm (GA) with the crop model AquaCrop is proposed for a location-specific calibration of maize cropping. In this approach, AquaCrop is used to simulate maize crop yield while the GA is used to derive optimal parameters set at grid cell resolution from various combinations of cultivar parameters and crop management in the process of crop and management options calibration. Statistics on pairwise simulated and observed yields indicate that the coefficient of determination varies from 0.20 to 0.65, with a yield deviation ranging from 8% to 36% across Burkina Faso (BF). An analysis of the optimal parameter sets shows that regardless of the climatic zone, a base temperature of 10？C and an upper temperature of 32？C is observed in at least 50% of grid cells. The growing season length and the harvest index vary significantly across BF, with the highest values found in the Soudanian zone and the lowest values in the Sahelian zone. Regarding management strategies, the fertility mean rate is approximately 35%, 39%, and 49% for the Sahelian, Soudano-sahelian, and Soudanian zones, respectively. The mean weed cover is around 36%, with the Sahelian and Soudano-sahelian zones showing the highest variability. The proposed approach can be an alternative to the conventional one-size-fits-all approach commonly used for regional crop modeling. Moreover, it has the potential to explore the performance of cropping strategies to adapt to changing climate conditions.
References
[1]
Janicot, S. (1992) Spatiotemporal Variability of West African Rainfall. Part I: Regionalizations and Typings. Journal of Climate, 5, 489-497. https://doi.org/10.1175/1520-0442(1992)005<0489:svowar>2.0.co;2
[2]
Nicholson, S.E. (2013) The West African Sahel: A Review of Recent Studies on the Rainfall Regime and Its Interannual Variability. ISRN Meteorology, 2013, 1-32. https://doi.org/10.1155/2013/453521
[3]
Partey, S.T., Zougmoré, R.B., Ouédraogo, M. and Campbell, B.M. (2018) Developing Climate-Smart Agriculture to Face Climate Variability in West Africa: Challenges and Lessons Learnt. Journal of Cleaner Production, 187, 285-295. https://doi.org/10.1016/j.jclepro.2018.03.199
[4]
Swinnen, J.F.M., Colen, L. and Maertens, M. (2013) Constraints to Smallholder Participation in High-Value Agriculture in West Africa. In: Elbehri, A., Ed., Rebuilding West Africa’s Food Potential, FAO/IFAD, 289-313.
[5]
Herrero, M., Thornton, P.K., Notenbaert, A.M., Wood, S., Msangi, S., Freeman, H.A., et al. (2010) Smart Investments in Sustainable Food Production: Revisiting Mixed Crop-Livestock Systems. Science, 327, 822-825. https://doi.org/10.1126/science.1183725
[6]
Kaminski, J., Elbehri, A. and Zoma, J.B. (2013) An Analysis of Maize Value Chain and Competitiveness in Burkina Faso: Implications for Smallholder-Inclusive Policies and Initiatives. In: Elbehri, A., Ed., Rebuilding WestAfrica’s Food Potential, FAO/IFAD, 453-478.
[7]
Zougmoré, R., Partey, S., Ouédraogo, M., Omitoyin, B., Thomas, T., Ayantunde, A., et al. (2016) Toward Climate-Smart Agriculture in West Africa: A Review of Climate Change Impacts, Adaptation Strategies and Policy Developments for the Livestock, Fishery and Crop Production Sectors. Agriculture & Food Security, 5, Article No. 26. https://doi.org/10.1186/s40066-016-0075-3
[8]
Biazin, B., Sterk, G., Temesgen, M., Abdulkedir, A. and Stroosnijder, L. (2012) Rainwater Harvesting and Management in Rainfed Agricultural Systems in Sub-Saharan Africa—A Review. Physics and Chemistry of the Earth, Parts A/B/C, 47, 139-151. https://doi.org/10.1016/j.pce.2011.08.015
[9]
Wall, E. and Smit, B. (2005) Climate Change Adaptation in Light of Sustainable Agriculture. Journal of Sustainable Agriculture, 27, 113-123. https://doi.org/10.1300/j064v27n01_07
[10]
Waongo, M., Laux, P. and Kunstmann, H. (2015) Adaptation to Climate Change: The Impacts of Optimized Planting Dates on Attainable Maize Yields under Rainfed Conditions in Burkina Faso. Agricultural and Forest Meteorology, 205, 23-39. https://doi.org/10.1016/j.agrformet.2015.02.006
[11]
Laux, P., Jäckel, G., Tingem, R.M. and Kunstmann, H. (2010) Impact of Climate Change on Agricultural Productivity under Rainfed Conditions in Cameroon—A Method to Improve Attainable Crop Yields by Planting Date Adaptations. Agricultural and Forest Meteorology, 150, 1258-1271. https://doi.org/10.1016/j.agrformet.2010.05.008
[12]
Waongo, M., Laux, P., Traoré, S.B., Sanon, M. and Kunstmann, H. (2014) A Crop Model and Fuzzy Rule Based Approach for Optimizing Maize Planting Dates in Burkina Faso, West Africa. Journal of Applied Meteorology and Climatology, 53, 598-613. https://doi.org/10.1175/jamc-d-13-0116.1
[13]
Coulibaly, A., Motelica-Heino, M. and Hien, E. (2019) Determinants of Agroecological Practices Adoption in the Sudano-Sahelian Zone. Journal of Environmental Protection, 10, 900-918. https://doi.org/10.4236/jep.2019.107053
[14]
McGuire, S. and Sperling, L. (2016) Seed Systems Smallholder Farmers Use. Food Security, 8, 179-195. https://doi.org/10.1007/s12571-015-0528-8
[15]
Atta-Aidoo, J., Antwi-Agyei, P., Dougill, A.J., Ogbanje, C.E., Akoto-Danso, E.K. and Eze, S. (2022) Adoption of Climate-Smart Agricultural Practices by Smallholder Farmers in Rural Ghana: An Application of the Theory of Planned Behavior. PLOS Climate, 1, e0000082. https://doi.org/10.1371/journal.pclm.0000082
[16]
Tapsoba, P.K., Aoudji, A.K.N., Ouédraogo, F., Dassekpo, I.S., Kestemont, M., Kabore Konkobo, M., et al. (2023) Understanding the Behavioral Drivers of Smallholder Agro-Ecological Practice Adoption in Benin and Burkina Faso. Farming System, 1, Article ID: 100023. https://doi.org/10.1016/j.farsys.2023.100023
[17]
Bombardi, R.J., Pegion, K.V., Kinter, J.L., Cash, B.A. and Adams, J.M. (2017) Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions. Frontiers in Earth Science, 5, Article 14. https://doi.org/10.3389/feart.2017.00014
[18]
Paeth, H. and Hense, A. (2003) Seasonal Forecast of Sub-Sahelian Rainfall Using Cross Validated Model Output Statistics. Meteorologische Zeitschrift, 12, 157-173. https://doi.org/10.1127/0941-2948/2003/0012-0157
[19]
Laux, P., Wagner, S., Wagner, A., Jacobeit, J., Bárdossy, A. and Kunstmann, H. (2009) Modelling Daily Precipitation Features in the Volta Basin of West Africa. International Journal of Climatology, 29, 937-954. https://doi.org/10.1002/joc.1852
[20]
Siegmund, J., Bliefernicht, J., Laux, P. and Kunstmann, H. (2015) Toward a Seasonal Precipitation Prediction System for West Africa: Performance of CFSV2 and High-Resolution Dynamical Downscaling. Journal of Geophysical Research: Atmospheres, 120, 7316-7339. https://doi.org/10.1002/2014jd022692
[21]
Bliefernicht, J., Waongo, M., Salack, S., Seidel, J., Laux, P. and Kunstmann, H. (2019) Quality and Value of Seasonal Precipitation Forecasts Issued by the West African Regional Climate Outlook Forum. Journal of Applied Meteorology and Climatology, 58, 621-642. https://doi.org/10.1175/jamc-d-18-0066.1
[22]
Hansen, J.W., Mason, S.J., Sun, L. and Tall, A. (2011) Review of Seasonal Climate Forecasting for Agriculture in Sub-Saharan Africa. Experimental Agriculture, 47, 205-240. https://doi.org/10.1017/s0014479710000876
[23]
Blundo-Canto, G., Andrieu, N., Soule Adam, N., Ndiaye, O. and Chiputwa, B. (2021) Scaling Weather and Climate Services for Agriculture in Senegal: Evaluating Systemic but Overlooked Effects. Climate Services, 22, Article ID: 100216. https://doi.org/10.1016/j.cliser.2021.100216
[24]
Roudier, P., Alhassane, A., Baron, C., Louvet, S. and Sultan, B. (2016) Assessing the Benefits of Weather and Seasonal Forecasts to Millet Growers in Niger. Agricultural and Forest Meteorology, 223, 168-180. https://doi.org/10.1016/j.agrformet.2016.04.010
[25]
Tankari, M.R. (2020) Rainfall Variability and Farm Households’ Food Insecurity in Burkina Faso: Nonfarm Activities as a Coping Strategy. Food Security, 12, 567-578. https://doi.org/10.1007/s12571-019-01002-0
[26]
Laudien, R., Schauberger, B., Waid, J. and Gornott, C. (2022) A Forecast of Staple Crop Production in Burkina Faso to Enable Early Warnings of Shortages in Domestic Food Availability. Scientific Reports, 12, Article No. 1638. https://doi.org/10.1038/s41598-022-05561-9
[27]
Ouedraogo, I., Savadogo, P., Tigabu, M., Cole, R., Odén, P.C. and Ouadba, J. (2009) Is Rural Migration a Threat to Environmental Sustainability in Southern Burkina Faso? Land Degradation & Development, 20, 217-230. https://doi.org/10.1002/ldr.910
[28]
Alene, A.D., Menkir, A., Ajala, S.O., Badu-Apraku, B., Olanrewaju, A.S., Manyong, V.M., et al. (2009) The Economic and Poverty Impacts of Maize Research in West and Central Africa. Agricultural Economics, 40, 535-550. https://doi.org/10.1111/j.1574-0862.2009.00396.x
[29]
Dembélé, M. and Zwart, S.J. (2016) Evaluation and Comparison of Satellite-Based Rainfall Products in Burkina Faso, West Africa. International Journal of Remote Sensing, 37, 3995-4014. https://doi.org/10.1080/01431161.2016.1207258
[30]
Ibrahim, B., Waongo, M., Sidibe, M., Sanfo, S. and Barry, B. (2022) Agroclimatological Characteristics of Rainy Seasons in Southwestern Burkina Faso during the 1970-2013 Period. Atmospheric and Climate Sciences, 12, 330-357. https://doi.org/10.4236/acs.2022.122021
[31]
De Longueville, F., Hountondji, Y., Kindo, I., Gemenne, F. and Ozer, P. (2016) Long-term Analysis of Rainfall and Temperature Data in Burkina Faso (1950-2013). International Journal of Climatology, 36, 4393-4405. https://doi.org/10.1002/joc.4640
[32]
Raes, D., Steduto, P., Hsiao, T.C. and Fereres, E. (2009) Aquacrop—The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Agronomy Journal, 101, 438-447. https://doi.org/10.2134/agronj2008.0140s
[33]
Raes, D., Waongo, M., Vanuytrecht, E. and Mejias Moreno, P. (2021) Improved Management May Alleviate Some but Not All of the Adverse Effects of Climate Change on Crop Yields in Smallholder Farms in West Africa. Agricultural and Forest Meteorology, 308, Article ID: 108563. https://doi.org/10.1016/j.agrformet.2021.108563
[34]
Steduto, P., Hsiao, T.C., Raes, D. and Fereres, E. (2009) Aquacrop—The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles. Agronomy Journal, 101, 426-437. https://doi.org/10.2134/agronj2008.0139s
[35]
Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., et al. (2021) The ERA5 Global Reanalysis: Preliminary Extension to 1950. Quarterly Journal of the Royal Meteorological Society, 147, 4186-4227. https://doi.org/10.1002/qj.4174
[36]
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020) The ERA5 Global Reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999-2049. https://doi.org/10.1002/qj.3803
[37]
Rolle, M., Tamea, S. and Claps, P. (2022) Climate-driven Trends in Agricultural Water Requirement: An ERA5-Based Assessment at Daily Scale over 50 Years. Environmental Research Letters, 17, Article ID: 044017. https://doi.org/10.1088/1748-9326/ac57e4
[38]
Rolle, M., Tamea, S. and Claps, P. (2021) ERA5-based Global Assessment of Irrigation Requirement and Validation. PLOS ONE, 16, e0250979. https://doi.org/10.1371/journal.pone.0250979
[39]
Maidment, R.I., Grimes, D., Black, E., Tarnavsky, E., Young, M., Greatrex, H., et al. (2017) A New, Long-Term Daily Satellite-Based Rainfall Dataset for Operational Monitoring in Africa. Scientific Data, 4, Article No. 170063. https://doi.org/10.1038/sdata.2017.63
[40]
Garba, J.N., Diasso, U.J., Waongo, M., Sawadogo, W. and Daho, T. (2023) Performance Evaluation of Satellite-Based Rainfall Estimation across Climatic Zones in Burkina Faso. Theoretical and Applied Climatology, 154, 1051-1073. https://doi.org/10.1007/s00704-023-04593-z
[41]
Reiter, P., Gutjahr, O., Schefczyk, L., Heinemann, G. and Casper, M. (2017) Does Applying Quantile Mapping to Subsamples Improve the Bias Correction of Daily Precipitation? International Journal of Climatology, 38, 1623-1633. https://doi.org/10.1002/joc.5283
[42]
Gia Pham, T., Kappas, M., Van Huynh, C. and Hoang Khanh Nguyen, L. (2019) Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam. ISPRS International Journal of Geo-Information, 8, Article 147. https://doi.org/10.3390/ijgi8030147
[43]
Ochoa-Rodriguez, S., Wang, L., Willems, P. and Onof, C. (2019) A Review of Radar-Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications. Water Resources Research, 55, 6356-6391. https://doi.org/10.1029/2018wr023332
[44]
Qiu, Q., Liu, J., Tian, J., Jiao, Y., Li, C., Wang, W., et al. (2020) Evaluation of the Radar QPE and Rain Gauge Data Merging Methods in Northern China. Remote Sensing, 12, Article 363. https://doi.org/10.3390/rs12030363
[45]
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998) Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements—FAO Irrigation and Drainage Paper 56. Food and Agriculture Organisation.
[46]
FAO and IIASA (2023) Harmonized World Soil Database version 2.0 [Internet]. FAO, International Institute for Applied Systems Analysis (IIASA). https://doi.org/10.4060/cc3823en
[47]
Dao, A., Sanou, J., Gracen, V. and Danquah, E.Y. (2015) Indentifying Farmers’ Preferences and Constraints to Maize Production in Two Agro-Ecological Zones in Burkina Faso. Agriculture & Food Security, 4, Article No. 13. https://doi.org/10.1186/s40066-015-0035-3
[48]
Yaméogo, I.S., Ouattara, D., Dabiré, R., Oumsonré, A., Kossi, L., Gnankiné, O., et al. (2023) Évaluation de la sensibilité de variétés vulgarisées de maïs à la chenille légionnaire d’automne, Spodoptera frugiperda J.E. Smith à l’ouest du Burkina Faso. Journal of Applied Biosciences, 191, 20186-20202. https://doi.org/10.35759/jabs.191.2
[49]
Boro, L.R. (2017) Diffusion de six variétés améliorées de maïs à travers la vitrine-test et les tests démonstratifs dans les cinq régions du Burkina Faso [Internet] [Diplome D’ingenieur du Developpement Rural]. Universite Nazi Boni (UNB).
[50]
Segda, Z. (1995) La culture du maïs dans les systèmes de culture pluviaux dans l’ouest du Burkina Faso. Production et valorisation du maïs à’ l’échelon villageois en Afrique de l’Ouest, CIRAD.
[51]
Badu-Apraku, B. and Fakorede, M.A.B. (2017) Climatology of Maize in Sub-Saharan Africa. In: Badu-Apraku, B. and Fakorede, M.A.B., Eds., Advances in Genetic Enhancement of Early and Extra-Early Maize for Sub-Saharan Africa, Springer, 11-31. https://doi.org/10.1007/978-3-319-64852-1_2
[52]
Rockström, J. and Barron, J. (2007) Water Productivity in Rainfed Systems: Overview of Challenges and Analysis of Opportunities in Water Scarcity Prone Savannahs. Irrigation Science, 25, 299-311. https://doi.org/10.1007/s00271-007-0062-3
[53]
Beasley, D., Bull, D.R. and Martin, R.R. (1993) An Overview of Genetic Algorithms: Part 1, Fundamentals. University Computing, 15, 56-69.
[54]
Holland, J.H. (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press. https://doi.org/10.7551/mitpress/1090.001.0001
[55]
Scrucca, L. (2013) GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53, 1-37. https://doi.org/10.18637/jss.v053.i04
[56]
Guo, D., Olesen, J.E., Pullens, J.W.M., Guo, C. and Ma, X. (2021) Calibrating Aquacrop Model Using Genetic Algorithm with Multi-Objective Functions Applying Different Weight Factors. Agronomy Journal, 113, 1420-1438. https://doi.org/10.1002/agj2.20588
[57]
Bagchi, T.P. (2012) Multiobjective Scheduling by Genetic Algorithms. Springer.
[58]
Dutta, S., Chakraborty, S., Goswami, R., Banerjee, H., Majumdar, K., Li, B., et al. (2020) Maize Yield in Smallholder Agriculture System—An Approach Integrating Socio-Economic and Crop Management Factors. PLOS ONE, 15, e0229100. https://doi.org/10.1371/journal.pone.0229100
[59]
Kraaijvanger, R. and Veldkamp, A. (2015) The Importance of Local Factors and Management in Determining Wheat Yield Variability in On-Farm Experimentation in Tigray, Northern Ethiopia. Agriculture, Ecosystems & Environment, 214, 1-9. https://doi.org/10.1016/j.agee.2015.08.003
[60]
van Loon, M.P., Adjei-Nsiah, S., Descheemaeker, K., Akotsen-Mensah, C., van Dijk, M., Morley, T., et al. (2019) Can Yield Variability Be Explained? Integrated Assessment of Maize Yield Gaps across Smallholders in Ghana. Field Crops Research, 236, 132-144. https://doi.org/10.1016/j.fcr.2019.03.022
[61]
van Wart, J., Kersebaum, K.C., Peng, S., Milner, M. and Cassman, K.G. (2013) Estimating Crop Yield Potential at Regional to National Scales. Field Crops Research, 143, 34-43. https://doi.org/10.1016/j.fcr.2012.11.018
[62]
Sirajul, I. and Bipul, T. (2014) Crop Yield Optimization Using Genetic Algorithm with the CROPWAT Model as a Decision Support System. CABI Databases, 7, 7-11.
