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Using the DSSAT Model to Support Decision Making Regarding Fertilizer Microdosing for Maize Production in the Sub-humid Region of Benin

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Tovihoudji, Pierre G. [Hydraulics and Environmental Modeling Laboratory, Université de Parakou, Bénin] Akponikpè, P. B. Irénikatché [Hydraulics and Environmental Modeling Laboratory, Université de Parakou, Bénin] Agbossou, Euloge K. [Laboratory of Hydraulics and Water Management, Université d'Abomey-Calavi, Cotonou, Bénin] Bielders, Charles [UCL]

Fertilizer microdosing is being widely promoted across sub-Saharan Africa, yet all recommendations regarding this technology are derived from short-term studies. Such studies are insufficient to properly assess the production risk caused by climatic variability. To address this issue while avoiding costly long-term experiments, a common and well accepted strategy is to combine results from short-term experiments with validated dynamic crop models. However, there have been few documented attempts so far to model fertilizer microdosing under sub-humid tropical conditions. The objective was therefore to evaluate the potential of the DSSAT model for simulating maize response to fertilizer microdosing, and to use the validated model to assess the effects of inter-annual rainfall variability on maize productivity and economic risk. The model was calibrated and validated against data from a 2-year on-station experiment (2014 and 2015) with 2 levels of hill-placedmanure and fivemineral fertilization options including broadcast and fertilizer microdosing. Model simulations were in good agreement with the observed grain and biomass yields for conventional broadcast fertilization, with relative RMSE and d-values of 12% and 0.96 for grain and 8% and 0.97 for biomass, respectively. For fertilizer microdosing, the N stress coefficient needed to be adjusted to avoid occurrence of large N stresses during simulation. After optimization, the model adequately reproduced grain yields for fertilizer microdosing, with relative RMSE of 10%. Considering the long-term scenario analysis, the use of the validated model showed that the application of 2 g of NPK15−15−15 fertilizer + 1 g urea per hill (equivalent to 23.8 kg N ha−1, 4.1 kg P ha−1 and 7.8 kg K ha−1) improved both the minimum guaranteed yield and the long-term average without increasing inter-annual variability and the economic risk compared to unfertilized plots. Even though combining microdosing with manure (1–3 t ha−1) was economically slightly riskier than microdosing alone, this risk remained low since a value-cost ratio of Tovihoudji et al. Simulating Maize Response to Microdosing 2 could be achieved in almost 100% of the years. Furthermore, combined application consistently reduced the inter-annual yield variability. Considering this as well as the other benefits of manure for soil health, combining microdosing with small quantities of manure would be recommended to increase the sustainability of the system.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès libre
Publication date 2019
Language Anglais
Journal information "Frontiers in Environmental Science" - Vol. 7, no.13, p. 1-16 (2019)
Peer reviewed yes
Publisher Frontiers Media SA (Bonn, Germany)
e-issn 2296-665X
Publication status Publié
Affiliation UCL - SST/ELI/ELIE - Environmental Sciences
Keywords Soil erosion ; Soil protection ; CERES-Maize ; Fertilizer microdosing ; Inter-annual yield variability ; Economic risk
Links
  1. Adnan Adnan A., Jibrin Jibrin M., Kamara Alpha Y., Abdulrahman Bassam L., Shaibu Abdulwahab S., Garba Ismail I., CERES–Maize Model for Determining the Optimum Planting Dates of Early Maturing Maize Varieties in Northern Nigeria, 10.3389/fpls.2017.01118
  2. Adnan Adnan A., Jibrin Jibrin M., Kamara Alpha Y., Abdulrahman Bassam L., Shaibu Abdulwahab S., Using CERES-Maize model to determine the nitrogen fertilization requirements of early maturing maize in the Sudan Savanna of Nigeria, 10.1080/01904167.2016.1263330
  3. Akponikpè P.B. Irénikatché, Gérard Bruno, Michels Karlheinz, Bielders Charles, Use of the APSIM model in long term simulation to support decision making regarding nitrogen management for pearl millet in the Sahel, 10.1016/j.eja.2009.09.005
  4. Akponikpè P.B.I., Minet J., Gérard B., Defourny P., Bielders C.L., Spatial fields’ dispersion as a farmer strategy to reduce agro-climatic risk at the household level in pearl millet-based systems in the Sahel: A modeling perspective, 10.1016/j.agrformet.2010.10.007
  5. Amouzou Kokou Adambounou, Naab Jesse B., Lamers John P.A., Becker Mathias, CERES-Maize and CERES-Sorghum for modeling growth, nitrogen and phosphorus uptake, and soil moisture dynamics in the dry savanna of West Africa, 10.1016/j.fcr.2017.12.017
  6. Aune Jens B., Bationo André, Agricultural intensification in the Sahel – The ladder approach, 10.1016/j.agsy.2008.05.002
  7. Aune Jens B., Doumbia Mamadou, Berthe Abou, Microfertilizing Sorghum and Pearl Millet in Mali : Agronomic, Economic and Social Feasibility, 10.5367/000000007781891504
  8. Bationo A., Waswa B.S., New Challenges and Opportunities for Integrated Soil Fertility Management in Africa, Innovations as Key to the Green Revolution in Africa (2011) ISBN:9789048125418 p.3-17, 10.1007/978-90-481-2543-2_1
  9. Camara, Int. J. AgriSci., 3, 387 (2013)
  10. From Agronomic Data to Farmer Recommendations: An Economics Training Manual (1988)
  11. Comoé Hermann, Siegrist Michael, Relevant drivers of farmers’ decision behavior regarding their adaptation to climate change: a case study of two regions in Côte d’Ivoire, 10.1007/s11027-013-9486-7
  12. Cooper P.J.M., Dimes J., Rao K.P.C., Shapiro B., Shiferaw B., Twomlow S., Coping better with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: An essential first step in adapting to future climate change?, 10.1016/j.agee.2008.01.007
  13. Corbeels Marc, Chirat Guillaume, Messad Samir, Thierfelder Christian, Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture, 10.1016/j.eja.2016.02.001
  14. FAO Statistical Database: Production and Trade2016
  15. Folberth Christian, Yang Hong, Gaiser Thomas, Abbaspour Karim C., Schulin Rainer, Modeling maize yield responses to improvement in nutrient, water and cultivar inputs in sub-Saharan Africa, 10.1016/j.agsy.2013.04.002
  16. Fosu-Mensah B. Y., MacCarthy D. S., Vlek P. L. G., Safo E. Y., Simulating impact of seasonal climatic variation on the response of maize (Zea mays L.) to inorganic fertilizer in sub-humid Ghana, 10.1007/s10705-012-9539-4
  17. Gijsman Arjan J., Hoogenboom Gerrit, Parton William J., Kerridge Peter C., Modifying DSSAT Crop Models for Low-Input Agricultural Systems Using a Soil Organic Matter–Residue Module from CENTURY, 10.2134/agronj2002.4620
  18. Gnanglè Césaire P., Glèlè Kakaï Romain, Assogbadjo Achille E., Vodounnon Samuel, Afouda Yabi Jacob, Sokpon Nestor, Tendances climatiques passées, modélisation, perceptions et adaptations locales au bénin, 10.4267/climatologie.259
  19. Guan Kaiyu, Sultan Benjamin, Biasutti Michela, Baron Christian, Lobell David B., Assessing climate adaptation options and uncertainties for cereal systems in West Africa, 10.1016/j.agrformet.2016.07.021
  20. Hafner H., George E., Bationo A., Marschner H., Effect of crop residues on root growth and phosphorus acquisition of pearl millet in an acid sandy soil in Niger, 10.1007/bf00779182
  21. Henao, Agricultural Production and Soil Nutrient Mining in Africa: Implications for Resource Conservation and Policy Development (2006)
  22. Hodge Angela, The plastic plant: root responses to heterogeneous supplies of nutrients, 10.1111/j.1469-8137.2004.01015.x
  23. Holzworth Dean P., Huth Neil I., deVoil Peter G., Zurcher Eric J., Herrmann Neville I., McLean Greg, Chenu Karine, van Oosterom Erik J., Snow Val, Murphy Chris, Moore Andrew D., Brown Hamish, Whish Jeremy P.M., Verrall Shaun, Fainges Justin, Bell Lindsay W., Peake Allan S., Poulton Perry L., Hochman Zvi, Thorburn Peter J., Gaydon Donald S., Dalgliesh Neal P., Rodriguez Daniel, Cox Howard, Chapman Scott, Doherty Alastair, Teixeira Edmar, Sharp Joanna, Cichota Rogerio, Vogeler Iris, Li Frank Y., Wang Enli, Hammer Graeme L., Robertson Michael J., Dimes John P., Whitbread Anthony M., Hunt James, van Rees Harm, McClelland Tim, Carberry Peter S., Hargreaves John N.G., MacLeod Neil, McDonald Cam, Harsdorf Justin, Wedgwood Sara, Keating Brian A., APSIM – Evolution towards a new generation of agricultural systems simulation, 10.1016/j.envsoft.2014.07.009
  24. Hoogenboom, Decision Support System For Agrotechnology Transfer (DSSAT). Version 4.6 (2015)
  25. Ibrahim Ali, Abaidoo Robert Clement, Fatondji Dougbedji, Opoku Andrews, Hill placement of manure and fertilizer micro-dosing improves yield and water use efficiency in the Sahelian low input millet-based cropping system, 10.1016/j.fcr.2015.04.022
  26. IBRAHIM A., PASTERNAK D., FATONDJI D., Impact of depth of placement of mineral fertilizer micro-dosing on growth, yield and partial nutrient balance in pearl millet cropping system in the Sahel, 10.1017/s0021859614001075
  27. Igué, Bull. Recherche Agron. Bénin, 2013, 24 (2013)
  28. Jones, CERES-Maize. A Simulation Model of Maize Growth and Development (1986)
  29. Jones J.W, Hoogenboom G, Porter C.H, Boote K.J, Batchelor W.D, Hunt L.A, Wilkens P.W, Singh U, Gijsman A.J, Ritchie J.T, The DSSAT cropping system model, 10.1016/s1161-0301(02)00107-7
  30. Keating B.A, Carberry P.S, Hammer G.L, Probert M.E, Robertson M.J, Holzworth D, Huth N.I, Hargreaves J.N.G, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes J.P, Silburn M, Wang E, Brown S, Bristow K.L, Asseng S, Chapman S, McCown R.L, Freebairn D.M, Smith C.J, An overview of APSIM, a model designed for farming systems simulation, 10.1016/s1161-0301(02)00108-9
  31. Kihara J., Huising J., Nziguheba G., Waswa B. S., Njoroge S., Kabambe V., Iwuafor E., Kibunja C., Esilaba A. O., Coulibaly A., Maize response to macronutrients and potential for profitability in sub-Saharan Africa, 10.1007/s10705-015-9717-2
  32. LIU Hai-long, YANG Jing-yi, HE Ping, BAI You-lu, JIN Ji-yun, Drury Craig F, ZHU Ye-ping, YANG Xue-ming, LI Wen-juan, XIE Jia-gui, YANG Jing-min, Hoogenboom Gerrit, Optimizing Parameters of CSM-CERES-Maize Model to Improve Simulation Performance of Maize Growth and Nitrogen Uptake in Northeast China, 10.1016/s2095-3119(12)60196-8
  33. Liu S., Yang J. Y., Drury C. F., Liu H. L., Reynolds W. D., Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada, 10.4141/cjss2013-096
  34. MacCarthy Dilys S., Adiku Samuel G. K., Freduah Bright S., Gbefo Francis, Kamara Alpha Y., Using CERES-Maize and ENSO as Decision Support Tools to Evaluate Climate-Sensitive Farm Management Practices for Maize Production in the Northern Regions of Ghana, 10.3389/fpls.2017.00031
  35. Impact of climate change on sorghum production under different nutrient and crop residue management in semi-arid region of Ghana: a modeling perspective275291 MacCarthyD. S. VlekP. L. G. Afr. Crop Sci. J.202012
  36. MacCarthy Dilys S., Vlek Paul L.G., Bationo A., Tabo R., Fosu M., Modeling nutrient and water productivity of sorghum in smallholder farming systems in a semi-arid region of Ghana, 10.1016/j.fcr.2010.06.005
  37. MacCarthy D. S., Vlek P. L. G., Fosu-Mensah B. Y., The Response of Maize to N Fertilization in a Sub-humid Region of Ghana: Understanding the Processes Using a Crop Simulation Model, Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT) (2012) ISBN:9789400729599 p.61-75, 10.1007/978-94-007-2960-5_5
  38. Marteau Romain, Sultan Benjamin, Moron Vincent, Alhassane Agali, Baron Christian, Traoré Seydou B., The onset of the rainy season and farmers’ sowing strategy for pearl millet cultivation in Southwest Niger, 10.1016/j.agrformet.2011.05.018
  39. Morris Michael, Kelly Valerie A., Kopicki Ron J., Byerlee Derek, Fertilizer Use in African Agriculture : Lessons Learned and Good Practice Guidelines, ISBN:9780821368800, 10.1596/978-0-8213-6880-0
  40. MUEHLIG-VERSEN B., BUERKERT A., BATIONO A., ROEMHELD V., PHOSPHORUS PLACEMENT ON ACID ARENOSOLS OF THE WEST AFRICAN SAHEL, 10.1017/s0014479703001261
  41. Ngwira A.R., Aune Jens B., Thierfelder C., DSSAT modelling of conservation agriculture maize response to climate change in Malawi, 10.1016/j.still.2014.05.003
  42. Nziguheba Generose, Zingore Shamie, Kihara Job, Merckx Roel, Njoroge Samuel, Otinga Abigael, Vandamme Elke, Vanlauwe Bernard, Phosphorus in smallholder farming systems of sub-Saharan Africa: implications for agricultural intensification, 10.1007/s10705-015-9729-y
  43. Okebalama Chinyere B., Safo Ebenezer Y., Yeboah Edward, Abaidoo Robert C., Logah Vincent, Fertilizer Microdosing in the Humid Forest Zone of Ghana: An Efficient Strategy for Increasing Maize Yield and Income in Smallholder Farming, 10.2136/sssaj2016.03.0065
  44. Pickering N. B., Hansen J. W., Jones J. W., Wells C. M., Chan V. K., Godwin D. C., WeatherMan: A Utility for Managing and Generating Daily Weather Data, 10.2134/agronj1994.00021962008600020023x
  45. Rezzoug, Afr. J. Agric. Res., 3, 284 (2008)
  46. Ritchie J. T., Soil water balance and plant water stress, Understanding Options for Agricultural Production (1998) ISBN:9789048149407 p.41-54, 10.1007/978-94-017-3624-4_3
  47. Saïdou A., Balogoun I., Ahoton E. L., Igué A. M., Youl S., Ezui G., Mando A., Fertilizer recommendations for maize production in the South Sudan and Sudano-Guinean zones of Benin, 10.1007/s10705-017-9902-6
  48. Sime Getachew, Aune Jens, Maize Response to Fertilizer Dosing at Three Sites in the Central Rift Valley of Ethiopia, 10.3390/agronomy4030436
  49. SOLER C. M. TOJO, BADO V. B., TRAORE K., BOSTICK W. MCNAIR, JONES J. W., HOOGENBOOM G., Soil organic carbon dynamics and crop yield for different crop rotations in a degraded ferruginous tropical soil in a semi-arid region: a simulation approach, 10.1017/s0021859611000050
  50. Tabo Ramadjita, Bationo Andre, Gerard Bruno, Ndjeunga Jupiter, Marchal Daniel, Amadou Bassirou, Garba Annou Mallam, Sogodogo Diakala, Taonda Jean-Baptiste Sibiry, Hassane Ousmane, Diallo Maimouna K., Koala Saidou, Improving cereal productivity and farmers’ income using a strategic application of fertilizers in West Africa, Advances in Integrated Soil Fertility Management in sub-Saharan Africa: Challenges and Opportunities ISBN:9781402057595 p.201-208, 10.1007/978-1-4020-5760-1_18
  51. Tittonell Pablo, Giller Ken E., When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture, 10.1016/j.fcr.2012.10.007
  52. Tovihoudji, Improving Maize Productivity in Northern Benin Through Localized Placement of Amendments and Fertilizers (2018)
  53. Tovihoudji Pierre G., Akponikpè P.B. Irenikatché, Agbossou Euloge K., Bertin Pierre, Bielders Charles L., Fertilizer microdosing enhances maize yields but may exacerbate nutrient mining in maize cropping systems in northern Benin, 10.1016/j.fcr.2017.08.003
  54. Tovihoudji, Variability in Maize Yield and Profitability Following Hill-Placement of Mineral Fertilizer and Manure Under Smallholder Farm Conditions in Northern Benin (2018)
  55. Turner Neil C., Rao K.P.C., Simulation analysis of factors affecting sorghum yield at selected sites in eastern and southern Africa, with emphasis on increasing temperatures, 10.1016/j.agsy.2013.06.002
  56. Twomlow Steve, Mugabe Francis T., Mwale Moses, Delve Robert, Nanja Durton, Carberry Peter, Howden Mark, Building adaptive capacity to cope with increasing vulnerability due to climatic change in Africa – A new approach, 10.1016/j.pce.2008.06.048
  57. Supplement to Agricultural Handbook No. 436 (1972)
  58. Vanlauwe B., Six J., Sanginga N., Adesina A. A., Soil fertility decline at the base of rural poverty in sub-Saharan Africa, 10.1038/nplants.2015.101
  59. Willmott Cort J., Ackleson Steven G., Davis Robert E., Feddema Johannes J., Klink Katherine M., Legates David R., O'Donnell James, Rowe Clinton M., Statistics for the evaluation and comparison of models, 10.1029/jc090ic05p08995
  60. Yakoub A., Lloveras J., Biau A., Lindquist J.L., Lizaso J.I., Testing and improving the maize models in DSSAT: Development, growth, yield, and N uptake, 10.1016/j.fcr.2017.07.002
  61. Yi, Plant Nutr. Fertil. Sci., 12, 294 (2006)
  62. Youssouf, Quatorzième réunion du sous-comité ouest et centre africain de corrélation des sols pour la mise en valeur des terres, 9-13 octobre 2000, 29 (2002)
Bibliographic reference Tovihoudji, Pierre G. ; Akponikpè, P. B. Irénikatché ; Agbossou, Euloge K. ; Bielders, Charles. Using the DSSAT Model to Support Decision Making Regarding Fertilizer Microdosing for Maize Production in the Sub-humid Region of Benin. In: Frontiers in Environmental Science, Vol. 7, no.13, p. 1-16 (2019)
Permanent URL http://hdl.handle.net/2078.1/212901