Decision Support Tools for Site-Specific Fertilizer Recommendations and Agricultural Planning in Selected Countries in Sub-Sahara Africa

  • Dilys S. MacCarthy
  • Job Kihara
  • Patricia Masikati
  • Samuel G. K. Adiku


Recommendations and decisions of crop management in sub-Saharan Africa (SSA) are often based on traditional field experimentation. This usually ignores the variability of production factors in space and time, and hence invalidates such decisions and recommendations outside of the experimental sites. Yet, the use of alternative or complementary decision support approaches such as crop modelling is limited. In this paper, we reviewed the state of the use of crop modelling in informing site specific fertilizer recommendations in some countries in SSA. Even though nitrogen fertilizer recommendations in most countries across Africa are blanket, the limited employment of models show that optimum nitrogen application should be differentiated according to soil types, management and climate. A number of studies reported on increased fertilizer use efficiency and reduced crop production risks with the use of Decision Support Tools (DST). The review also showed that the gross limitation of the use of models as agricultural decision-making tools in SSA could be attributed to factors such as low capacity due to limited training opportunities, and the general lack of support from national governments for model development and application for policy formulation. Proposals identified to overcome these limitations include (1) introduction of the science of DST in the curricula at the tertiary level, (2) encouragement and support for the adoption of model use by governmental and non-governmental organizations as additional tools for decision making and (3) simplifying DSTs to facilitate their use by non-scientific audience to scale uptake and use for farm management.


Risk management Resource use efficiency Sub Sahara Africa Soil productivity 


  1. Adiku, S. G. K. (1995). A field investigation and modelling the effects of soil, climate and management factors on the growth of maize-cowpea intercrop. Ph.D. thesis, School of Environmental Sciences, Griffith University, Australia, 458 p.Google Scholar
  2. Adiku, S. G. K., Rose, C. W., Gabric, A., Braddock, R. D., Carberry, P. S., & McCown, R. L. (1998). An evaluation of the performance of maize and cowpea in sole and intercropping systems at two savannah zones in Ghana: A simulation study. ‘MODEL–IT applications of modelling as an innovative technology in the Agri–Food Chain’. Acta Horticulturae, 476, 251–259.CrossRefGoogle Scholar
  3. Adiku, S. G. K., MacCarthy, D. S., Haithie, I., Diancoumba, M., Freduah, B. S., Amikuzuno, J., Traore, P. C. S., Traore, S., Koomson, E., Agali, A., Lizaso, J., Fatondji, D., Adams, M., Tigana, L., Diarra, D. Z., & N’diaye, O. (2015). Climate change impacts on West African agriculture: An integrated regional assessment. In C. Rosenzweig & D. Hillel (Eds.), Handbook of climate change and agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments – Joint Publication with the American Society Of Agronomy, Crop Science Society of America and Soil Science Society of America (pp. 25–73). London: Imperial College Press.Google Scholar
  4. Adnan, A. A., Jibrin, M. J., Kamara, A. Y., Abdulrahman, B. L., & Shaibu, A. W. S. (2017). Using CERES–Maize model to determine the nitrogen fertilization requirements of early maturing maize in the Sudan Savanna of Nigeria. Journal of Plant Nutrition, 40(7), 1066–1082.CrossRefGoogle Scholar
  5. Akponikpe, P., Gérard, B., Michels, K., & Bielders, C. (2010). Use of the APSIM model in long term simulation to support decision making regarding nitrogen management for pearl millet in the Sahel. European Journal of Agronomy, 32, 144–154.CrossRefGoogle Scholar
  6. Akponikpe, P. B. I., Gerald, B., & Bielders, C. L. (2014). Soil water crop modeling for decision support in millet-based systems in the Sahel: a challenge. African Journal of Agricultural Research, 9(22), 1700–1713.CrossRefGoogle Scholar
  7. Araya, A., Habtu, S., Hadgu, K. M., Kebede, K., & Dejene, T. (2010). Test of AquaCrop modeling simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agricultural Water Management, 97, 1838–1846.CrossRefGoogle Scholar
  8. Atakora, W. K., Fosu, M., & Marthey, F. (2014). Modeling maize production towards site specific fertilizer recommendation in Ghana. Global Journal of Science Frontier Research: D Agriculture and Veterinary, 14(6), 70–81.Google Scholar
  9. Bationo, A., & Buekert, A. (2001). Soil organic carbon management for sustainable land use in Sudano-Sahelian West Africa. Nutrient Cycling in Agroecosystems, 61, 131–142.CrossRefGoogle Scholar
  10. Beletse, Y. G., Laurie, R., du Plooy, C. P., van den Berg, A., & Laurie, S. (2011). Calibration and validation of AquaCrop model for orange fleshed sweet potatoes. In R. Ardakanian, & T. Walter (Eds.), Capacity development for farm management strategies to improve crop water productivity using AquaCrop: Lessons learned. UNW-DPC Publication Series, Knowledge No 7, Bonn.Google Scholar
  11. Beletse, Y. G., Durand, W., Nhemachena, C., Crespo, O., Tesfuhuney, W. A., Jones, M. R., Teweldemedhin, M. Y., Gamedze, S. M., Bonolo, P. M., Jonas, S., Walker, S., Gwimbi, P., Mpuisang, T. N., Cammarano, D., & Valdivia, R. O. (2015). Projected impacts of climate change scenarios on the production of maize in Southern Africa: An integrated assessment case study of Bethlehem District, Central Free State, South Africa. In C. Rosenzweig & D. Hillel (Eds.), Handbook of climate change and agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments – Joint Publication with the American Society Of Agronomy, Crop Science Society of America, and Soil Science Society of America (pp. 125–158). London: Imperial College Press.Google Scholar
  12. Bontkes, S. T. E., Wopereis, M. C., Tamelokpo, A., Ankou, K. A., & Lamboni, D. (2003). The use of QUEFTS in search of balanced fertilizer recommendations for maize in Togo. In S. T. E. Bontkes & M. C. S. Wopereis (Eds.), Decision support tools for smallholder agriculture in Sub Saharan Africa IFDC (pp. 68–84). IFDC Muscle Shoals, USA, and CTA. Wageningen, The Netherlands.Google Scholar
  13. Brisson, N., Bruno, M., Ripoche, D., Jeuffroy, M. H., Ruget, F., Nicoullaud, B., Gate, P., Devienne-Barret, F., Antonioletti, R., Durr, C., Richard, G., Beaudoin, N., Recous, S., Tayot, X., Plenet, D., Cellier, P., Machet, J.-M., Meynard, J. M., & Delécolle, R. (1998). STICS: A generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie, 18(5–6), 311–346.Google Scholar
  14. Carberry, P. S., Hochman, Z., McCown, R. L., Dalgliesh, N. P., Foale, M. A., Poulton, P. L., Hargreaves, J. N. G., Hargreaves, D. M. G., Cawthray, S., Hillcoat, N., & Robertson, M. J. (2002). The FARMSCAPE approach to decision support: farmers’, advisers’, researchers’ monitoring, simulation, communication and performance evaluation. Agricultural Systems, 74, 141–177.CrossRefGoogle Scholar
  15. Chikowo, R., Corbeels, M., Tittonell, P., Vanlauwe, B., Whitbread, A., & Giller, K. E. (2008). Aggregating field-scale knowledge into farm-scale models of African smallholder systems: Summary functions to simulate crop production using APSIM. Agricultural Systems, 97, 151–166.CrossRefGoogle Scholar
  16. Chimonyo, V. G. P., Modi, A. T., & Mabhaudhi, T. (2016). Water use and productivity of a sorghum–cowpea–bottle gourd intercrop system. Agricultural Water Management, 165, 82–96.CrossRefGoogle Scholar
  17. Chisanga, C. B., Phiri, E., Shepande, C., & Sichingabula, H. (2015). Evaluating CERES-maize model using planting dates and nitrogen fertilizer in Zambia. Journal of Agricultural Science, 7(3), 79–97.CrossRefGoogle Scholar
  18. Comprehensive Africa Agriculture Development Programme (CAADP). (2003). The African Union Summit declaration on CAADP. Maputo, Mozambique. ISBN 0-620-30700-5.
  19. de Jager, A., Kariuku, I., Matiri, F. M., Odendo, M., & Wanyama, J. M. (1998). Monitoring nutrient flows and economic performance in African farming systems (NUTMON): IV. Linking nutrient balances and economic performance in three districts in Kenya. Agriculture, Ecosystems & Environment, 71(1–3), 81–92.CrossRefGoogle Scholar
  20. de Wit, C. T. (1958). Transpiration and crop yields. Volume 64 of Agricultural research report/Netherlands Volume 59 of Mededeling (Instituut voor Biologisch en Scheikundig Onderzoek va Landbouwgewasses) Verslagen van landbouwkundige onderzoekingen. Institute of Biological and Chemical Research on Field Crops and Herbage.Google Scholar
  21. Delve, R. J., Robert, M. E., Cobo, J. G., Ricaurte, J., Rivera, M., Barrios, E., & Rao, I. M. (2009). Simulating phosphorus responses in annual crops using APSIM: Model evaluation on contrasting soil types. Nutrient Cycling in Agroecosystems, 84, 293–306.CrossRefGoogle Scholar
  22. Diarisso, T., Corbeels, M., Andrieu, N., Djamen, P., Douzet, J., & Tittonell, P. (2015). Soil variability and crop yield gaps in two village landscapes of Burkina Faso. Nutrient Cycling in Agroecosystems.
  23. Dzotsi, K., Agboh-Noaméshie, A., Struif Bontkes, T. E., Singh, U., & Dejean, P. (2003). Using DSSAT to derive optimum combinations of cultivar and sowing date for Maize in Southern Togo. In: S. Bontkes, T. E. Wopereis (Eds.), Decision support tools for smallholder agriculture in Sub Saharan Africa IFDC (pp 100–112). IFDC Muscle Shoals, USA, and CTA, Wageningen, The Netherlands.Google Scholar
  24. Dzotsi, K. A., Jones, J. W., Adiku, S. G. K., Naab, J. B., Singh, U., Porter, C. H., & Gijsman, A. J. (2010). Modelling soil and plant phosphorus within DSSAT. Ecological Modelling, 221, 2839–2849.CrossRefGoogle Scholar
  25. Estes, L. D., Beukes, H., Bradley, B. A., Rdebats, S., Oppenheimer, M., Ruane, A. C., Schulze, R., & Tadross, M. (2013). Projected climate impacts to South African maize and wheat production in 2055: A comparison of empirical and mechanistic modeling approaches. Global Change Biology, 19, 3762–3774. (1-13).
  26. FAO. (2006). Chapter 6: Fertilizer use by crop in Zimbabwe. Fertilizer-Use Recommendations Food and Agriculture Organization of the United Nations, Rome. Available at:
  27. Fatondji, D., Bationo, A., Tabo, A., Jones, J. W., Adamou, A., & Hassane, O. (2012). Water use and yield of millet under the zai system: Understanding the processes using simulation. In Improving soil fertility recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT) (pp. 77–100). Dordrecht: Springer.
  28. Folberth, C., Yang, H., Gaiser, T., Abbaspour, K. C., & Schulin, R. (2013). Modeling maize yield responses to improvement in nutrient, water and cultivar inputs in sub-Saharan Africa. Agriculture Systems, 119, 22–34.CrossRefGoogle Scholar
  29. Fosu, M., Buah, S. S., Kanton, R. A. L., & Agyare, W. A. (2012). Modelling Maize response to mineral fertilizer on silty clay loam in the Northern Savanna of Ghana Using DSSAT model. In J. Kihara, D. Fatondji, J. W. Jones, G. Hoogenboom, R. Tabo, & A. Bationo (Eds.), Improving soil fertility recommendations in Africa using the Decision Support Systems for Agro–technology Transfer (DSSAT) (pp. 157–168). New York: Springer Science + Business Media B. V.CrossRefGoogle Scholar
  30. Fosu-Mensah, B. Y., MacCarthy, D. S., Vlek, P. L. G., & Safo, E. Y. (2012). Simulating impact of seasonal climatic variation on the response of maize (Zea mays L.) to inorganic fertilizer in sub–humid Ghana. Nutrient Cycling in Agroecosystems, 94, 255–271.CrossRefGoogle Scholar
  31. Gaiser, T., de Barros, I., Sereke, F., & Lange, F. M. (2010). Validation and reliability of the EPIC model to simulate maize production in small-holder farming systems in tropical sub-humid West Africa and semi-arid Brazil. Agriculture, Ecosystem and Environment, 135, 318–327.CrossRefGoogle Scholar
  32. Gungula, D. J., Kling, J. G., & Togun, A. O. (2003). CERES–maize predictions of maize phenology under nitrogen-stressed conditions in Nigeria. Agronomy Journal, 95, 892–899.CrossRefGoogle Scholar
  33. Haefele, S. M., Wopereis, M. C. S., Ndiaye, M. K., & Kropff, M. J. (2003). A framework to improve fertilizer recommendations for irrigated rice in West Africa. Agricultural Systems, 76(1), 313–335.CrossRefGoogle Scholar
  34. Haefele, S. M., Sipaseuth, N., Phengsouvanna, V., Dounphady, K., & Vongsouthi, S. (2010). Agro–economic evaluation of fertilizer recommendations for rainfed lowland rice. Field Crops Research, 119, 215–224.CrossRefGoogle Scholar
  35. Hansen, J. W. (2005). Integrating seasonal climate prediction and agricultural models for insights into agricultural practice. Philosophical Transactions. Royal Society of Britain, 360, 2037–2047. Scholar
  36. Hansen, J. W., Mishra, A., Rao, K. P. C., Indeje, M., & Ngugi, R. K. (2009). Potential value of GCM-based seasonal rainfall forecasts for maize management in semi-arid Kenya. Agricultural Systems, 101, 80–90.CrossRefGoogle Scholar
  37. Hess, T. M., Stephens, W., Crout, N. M. J., Young, S. D., & Bradley, R. G. (1997). Predicting Arable Resources in Hostile environments (PARCH), User Guide. Natural Resource Institute, Chatham.Google Scholar
  38. Hoogenboom, G., Jones, J. W., Traore, P. C. S., & Boote, K. J. (2012). Experiments and data for model evaluation and application; Understanding the Processes using a Crop Simulation Model. In J. Kihara, D. Fatondji, J. W. Jones, G. Hoogenboom, R. Tabo, & A. Bationo (Eds.), Improving Soil Fertility Recommendations in Africa using the Decision Support Systems for Agrotechnology Transfer (DSSAT) (pp. 9–18). Springer Science + Business Media B. V.Google Scholar
  39. Huth, N. I., Carberry, P. S., Poulton, P. L., Brennan, L. E., & Keating, B. A. (2003). A framework for simulating agroforestry options for the low rainfall areas of Australia using APSIM. European Journal of Agronomy, 18, 171–185.CrossRefGoogle Scholar
  40. Jagtap, S. S., Abamu, F. J., & Kling, J. G. (1999). Long-term assessment of nitrogen and variety technologies on attainable maize yields in Nigeria using CERES-maize. Agricultural Systems, 60, 77–86.CrossRefGoogle Scholar
  41. Jansen, B. H., Guiking, F. C. T., van der Eijk, D., Smaling, E. M. A., Wolf, J., & van Reuler, H. (1990). A system for quantitative evaluation of the fertility of tropical soils (QUEFTS). Geoderma, 46, 299–318.CrossRefGoogle Scholar
  42. Jones, P. G., & Thornton, P. K. (2003). The potential impacts of climate change on maize production in Africa and Latin America in 2055. Global Environmental Change, 13, 51–59.CrossRefGoogle Scholar
  43. 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. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3–4), 235–265.CrossRefGoogle Scholar
  44. Jones, J. W., Antle, J. M., Bruno, B., Boote, K. J., Conant, R. T., Ian Foster, I., Godfray, H. C. J., Mario, H. M., Howitt, R. E., Janssen, S., Keating, B. A., Munoz-Carpena, R., Porter, C. H., Rosenzweig, C., & Wheeler, T. R. (2017). Brief history of agricultural systems modeling. Agricultural Systems.
  45. Karunaratne, A. S., Azam-Ali, S. N., Izzi, G., & Steduto, P. (2011). Calibration and validation of FAO-AquaCrop model for irrigated and water deficient Bambara groundnut. Experimental Agriculture, 47, 509–527. Scholar
  46. Kassie, B. T., Van Ittersum, M. K., Hengsdijk, H., Asseng, S., Wolf, J., & Rötter, R. P. (2014). Climate – induced yield variability and yield gaps of maize (Zea mays L) in Central Rift Valley of Ethiopia. Field Crops Research, 160, 41–53.CrossRefGoogle Scholar
  47. Kassie, B. T., Asseng, S., Rötter, R. P., Hengsdijk, H., Ruane, A. C., & van Ittersum, M. K. (2015). Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Climatic Change, 129(1–2), 145–158. Scholar
  48. Katambara, Z., Kahimba, F. C., Mbungu, W. B., Reuben, P., Maugo, M., Mhenga, F. D., & Mahoo, H. F. (2013). Optimizing system of rice intensification parameters using AquaCrop model for increasing water productivity and water use efficiency on rice production in Tanzania. Journal of Agriculture and Sustainability, 4(2), 235–244.Google Scholar
  49. Keating, B. A., Godwin, D. C., & Watiki, J. M. (1991). Optimization of nitrogen inputs under climatic risk. In R. C. Muchow & J. A. Bellamy (Eds.), Climatic risk in crop production: Models and management for the semiarid tropics and subtropics (pp. 329–357). Wallingford: CAB International.Google Scholar
  50. 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. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, 267–288.CrossRefGoogle Scholar
  51. Kihara, J., Fatondji, D., Jones, J. W., Hoogenboom, G., Tabo, R., & Bationo, A. (2012). Improving soil fertility recommendations in Africa using the Decision Support Systems for Agro-technology Transfer (DSSAT). Springer, 184 p.Google Scholar
  52. Kihara, J., Huising, J., Nziguheba, G., Waswa, B. S., Njoroge, S., Kabambe, V., Iwuafor, E., Kibunja, C., Esilaba, A. O., & Coulibaly, A. (2015). Maize response to macronutrients and potential for profitability in sub-Saharan Africa. Nutrient Cycling in Agroecosystems, 105, 171–181. Scholar
  53. Kipkorir, E. C., Mugalavai, E. M., & Bargerei, R. J. (2010). Application of AquaCrop model for within-season prediction of grain maize yields. Presented at a Workshop on “Improving farm management strategies through AquaCrop: Worldwide collection of case studies”, October 8–9, 2010, Yogyakarta.Google Scholar
  54. Kisaka, M. O., Mucheru-Muna, M., Ngetich, F. K., Mugwe, J. N., Mugendi, D. N., Mairura, F., & Muriuki, J. (2015). Using APSIM-model as a decision-support-tool for long-term integrated-nitrogen-management and maize productivity under semi-arid conditions in Kenya. In Experimental Agriculture (pp. 1–12). Cambridge University Press.
  55. Kurwakumire, N., Chikowo, R., Mtambanengwe, F., Mapfumo, P., Snapp, S., Johnston, A., & Zingore, A. (2014). Maize productivity and nutrient and water use efficiencies across soil fertility domains on smallholder farms in Zimbabwe. Field Crops Research.
  56. La Maran, R., & Leatherman, D. A. (1992). NUMAS – A nutrient management advisory system. Technical summary and user manual. Urbana: The knowledge Based Systems Research Lab, University of Illinois.Google Scholar
  57. Littleboy, M., Silburn, D. M., Freebairn, D. M., Woodruff, D. R., & Hammer, G. L. (1989). PERFECT: A computer simulation model of Productivity Erosion Runoff Functions to Evaluate Conservation Techniques (119 p). Queensland Department of Primary Industries, Australia: Brisbane.Google Scholar
  58. Mabhaudhi, T., Modia, A. T., & Beletse, Y. G. (2014). Parameterisation and evaluation of the FAO–AquaCrop model for a South African taro (Colocasia esculenta L. Schott) landrace. Agricultural and Forest Meteorology, 193, 132–139.CrossRefGoogle Scholar
  59. MacCarthy, D. S., Sommer, R., & Vlek, P. L. G. (2009). Modeling the impacts of contrasting nutrient and residue management practices on grain yield of sorghum (Sorghum bicolor (L) Moench) in a semi-arid region of Ghana using APSIM. Field Crops Research, 113, 105–115.CrossRefGoogle Scholar
  60. MacCarthy, D. S., Vlek, P. L. G., Bationo, A., Tabo, R., & Fosu, M. (2010). Modeling nutrient and water productivity of sorghum in smallholder farming systems in a semi-arid region of Ghana. Field Crops Research, 118(3), 251–258.CrossRefGoogle Scholar
  61. MacCarthy, D. S., Vlek, P. L. G., & Fosu-Mensah, B. Y. (2012). The response of maize to N fertilization in a sub–humid region of Ghana; understanding the processes using a crop simulation model. In J. Kihara, D. Fatondji, J. W. Jones, G. Hoogenboom, R. Tabo, & A. Bationo (Eds.), Improving soil fertility recommendations in Africa using the Decision Support Systems for Agrotechnology Transfer (DSSAT) (pp. 157–168). Springer.Google Scholar
  62. MacCarthy, D. S., Akponikpe, P. B. I., Narh, S., & Tegbe, R. (2015). Modelling the effect of seasonal climate variability on the efficiency of mineral fertilization on maize in the coastal savannah of Ghana. Nutrient Cycling in Agroecosystems, 102, 45–64.CrossRefGoogle Scholar
  63. MacCarthy, D. S., Adiku, S. G. K., Freduah, B. S., Gbefo, F., & Kamara, A. Y. (2017). 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. Frontiers in Plant Science, 8, 31. Scholar
  64. Masanganise, J., Basira, K., Chipindu, B., Mashonjowa, E., & Mhizha, T. (2013). Testing the utility of a crop growth simulation model in predicting maize yield in a changing climate in Zimbabwe. International Journal of Agriculture and Food Science, 3(4), 157–163.Google Scholar
  65. Masikati, P., Manschadi, A., van Rooyen, A., & Hargreaves, J. (2014). Maize–mucuna rotation: An alternative technology to improve water productivity in smallholder farming systems. Agricultural Systems, 123, 62–70.CrossRefGoogle Scholar
  66. Masikati, P., Homann-Kee, T. S., Descheemaeker, K., Crespo, O., Walker, S., Lennard, C. J., Claessens, L., Gama, A. C., Famba, S., van Rooyen, A. F., & Valdivia, R. O. (2015). Crop-livestock intensification in the face of climate change: Exploring opportunities to reduce risk and increase resilience in Southern Africa by using an integrated multi-modeling approach. In C. Rosenzweig & D. Hillel (Eds.), Handbook of climate change and agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments–Joint Publication with the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America (pp. 159–198). London: Imperial College Press.Google Scholar
  67. McCown, R. L., Wafula, B. M., Mohammed, L., Ryan, J. G., & Hargreaves, J. N. G. (1992). Assessing the value of a seasonal rainfall predictor to agronomic decisions: The case of response farming in Kenya. In R. C. Muchow & J. A. Bellamy (Eds.), Climatic risk in crop production: Models and management for the semi-arid tropics and subtropics (pp. 383–409). Wallingford: CAB International, Wallingford.Google Scholar
  68. Mhizha, T., Geerts, S., Vanuytrecht, E., Makarau, A., & Raes, D. (2014). Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe. Water SA, 40(2).
  69. Micheni, A. N., Kihanda, F. M., Warren, G. P., & Probert, M. E. (2004). Testing the APSIM model with experimental data from the long-term manure experiment at Machang’a, Kenya. In R. J. Delve & M. E. Probert (Eds.), Modelling nutrient management in tropical cropping systems (pp. 110–117). Canberra: ACIAR. Proceedings No 114.Google Scholar
  70. Mitscherlich, E. A. (1913). Soil science for agriculture and forestry (2nd ed.). Berlin: Verlag Paul Parey.Google Scholar
  71. Mowo, J. G., Janssen, B. H., Oenema, O., German, L. A., Mrema, J. P., & Shemdoe, R. S. (2006). Soil fertility evaluation and management by smallholder farmer communities in northern Tanzania. Agriculture, Ecosystem and Environment, 116, 47–59.CrossRefGoogle Scholar
  72. Mugalavai, E. M., & Kipkorir, E. C. (2015). Robust method for estimating grain yield in western Kenya during the growing seasons. Journal of Water and Climate Change, 6(2), 313–324.CrossRefGoogle Scholar
  73. Mupangwa, W., & Jewitt, G. P. W. (2011). Simulating the impact of no-till systems on field water fluxes and maize productivity under semi-arid conditions. Physics Chem Earth, 36, 1004–1011.CrossRefGoogle Scholar
  74. Naab, J. B., Mahama, G. Y., Koo, J., Jones, J. W., & Boote, K. (2015). Nitrogen and phosphorus fertilization with crop residue retention enhances crop productivity, soil organic carbon, and total soil nitrogen concentrations in sandy–loam soils in Ghana. Nutrient Cycling in Agroecosystems, 102, 33–43.CrossRefGoogle Scholar
  75. Ncube, B., Dimes, J. P., van Wijk, M., Twomlow, S., & Giller, K. E. (2009). Productivity and residual benefits of grain legumes to sorghum under semi-arid conditions in south-western Zimbabwe: Unravelling the effects of water and nitrogen using a simulation model. Field Crops Research, 110(1), 173–184.CrossRefGoogle Scholar
  76. Ngwira, A., Aune, J., & Thierfelder, C. (2014). DSSAT modelling of conservation agriculture maize response to climate change in Malawi. Soil Tillage Research, 143, 85–94.CrossRefGoogle Scholar
  77. Nurudeen, A. R. (2014). Decision Support System for Agro-technology Transfer (DSSAT) model simulation of maize growth and yield response to NPK fertilizer application on a benchmark soil of Sudan Savanna Agro-ecological Zone of Ghana. MSc. thesis, Kwame Nkrumah University of Science and Technology Kumasi.Google Scholar
  78. Nyakudya, I. W., & Stroosnijder, L. (2014). Effect of rooting depth, plant density and planting date on maize (Zea mays L) yield and water use efficiency in semi-arid Zimbabwe: Modelling with AquaCrop. Agriculture Water Management, 146, 280–296.CrossRefGoogle Scholar
  79. O’Leary, G. J. (2000). A review of three sugarcane simulation models with respect to their prediction of sucrose yield. Field Crops Research, 68, 97–111.CrossRefGoogle Scholar
  80. Okwach, G. E., & Simiyu, C. S. (1999). Evaluation of long-term effects of management on land productivity in a semi-arid are of Kenya using simulation models. East African Agriculture. Forestry Journal, 65, 143–155.Google Scholar
  81. Parton, W. J., & Rasmussen, P. E. (1994). Long-term effects of crop management in Wheat-Fallow: II. CENTURY model simulations. Soil Science Society of America Journal, 58, 530–536.CrossRefGoogle Scholar
  82. Raes, D. P., Steduto, P., Hsiao, T. C., & Fereres, E. (2009). AquaCrop—The FAO crop model to predict yield response to water: II Main algorithms and soft ware description. Agronomy Journal, 101, 438–447.CrossRefGoogle Scholar
  83. Rao, K. P. C., Sridhar, G., Mulwa, R. M., Kilavi, M. N., Esilaba, A., Athanasiadis, I. N., & Valdivia, R. O. (2015). Impacts of climate variability and change on agricultural systems in East Africa. In C. Rosenzweig & D. Hillel (Eds.), Handbook of climate change and agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments – Joint Publication with the American Society Of Agronomy, Crop Science Society of America, and Soil Science Society of America (pp. 75–124). London: Imperial College Press.Google Scholar
  84. Robertson, M. J., Sakala, W., Benson, T., & Shamudzarira, Z. (2005). Simulating response of maize to previous velvet bean (Mucuna pruriens) crop and nitrogen fertilizer in Malawi. Field Crops Research, 91, 91–105.CrossRefGoogle Scholar
  85. Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., Twining, S., Foulkes, C., Amano, T., & Dicks, L. V. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165–174.CrossRefGoogle Scholar
  86. Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., Asseng, S., Basso, B., Ewert, F., Wallach, D., Baigorria, G., & Winter, J. M. (2013). The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agriculture and Forest Meteorology, 170, 66–182.CrossRefGoogle Scholar
  87. Saito, K., Diack, S., Dieng, I., & N’Diaye, M. K. (2015). On-farm testing of a nutrient management decision-support tool for rice in the Senegal River valley. Computers and Electronics in Agric Archive, 116, 36–44. Scholar
  88. Sanchez, P. A. (2002). Soil fertility and hunger in Africa. Science, 295, 2019–2020.CrossRefPubMedGoogle Scholar
  89. Sanchez, P., Denning, G., & Nziguheba, G. (2009). The African green revolution moves forward. Food Security, 1, 37–44.CrossRefGoogle Scholar
  90. Schultze, R. E. (1975). Catchment evapotranspiration in the Natal Drakensberg. Ph. D. thesis, Department of Geography, University of Natal, Pietermaritzburg, RSA. 244 p.Google Scholar
  91. Segda, Z., Haefele, S. M., Wopereis, M. C. S., Sedogo, M. P., & Guinko, S. (2005). Combining field and simulation studies to improve fertilizer recommendations for irrigated rice in Burkina Faso. Agronomy Journal, 97, 1429–1437.CrossRefGoogle Scholar
  92. Shaffer, M. J., Gupta, S. C., Linden, D. R., Molina, J. A. E., Clapp, C. E., & Larson, W. E. (1983). Simulation of nitrogen, tillage, and residue management effects on soil fertility. In W. K. Lauenroth, G. V. Skogerboe, & M. Flug (Eds.), Analysis of ecological systems: State-of-the-art in ecological modelling. Developments in environmental modelling, 5 (pp. 525–544). Amsterdam: Elsevier.Google Scholar
  93. Singels, A., & Bezuidenhout, C. N. (2002). A new method of simulating dry matter partitioning in the CANEGRO sugarcane model. Field Crops Research, 78, 151–164.CrossRefGoogle Scholar
  94. Smaling, E. M. A., & Fresco, L. O. (1993). A decision–support model for monitoring nutrient balances under agricultural land use (NUTMON). Geoderma, 60(1–4), 235–256.CrossRefGoogle Scholar
  95. Smaling, E. M. A., & Janssen, B. H. (1993). Calibration of QUEFTS, a model predicting nutrient uptake and yields from chemical soil fertility indices. Geoderma, 59, 21–44.CrossRefGoogle Scholar
  96. Srivastava, A. K., Gaiser, T., Cornet, D., & Ewert, F. (2012). Estimation of effective fallow availability for the prediction of yam productivity at the regional scale using model-based multiple scenario analysis. Field Crops Research, 131, 32–39.CrossRefGoogle Scholar
  97. Stöckle, C. O., Donatelli, M., & Nelson, R. (2003). CropSyst, a cropping systems simulation model. European Journal of Agronomy, 18(3-4), 289–307.CrossRefGoogle Scholar
  98. Stoorvogel, J. J., & Smaling, E. M. A. (1990). Assessment of soil nutrient depletion in Sub-Saharan Africa: 1983–2000. Vol. 2: 28 Nutrient balances per crop and per land use systems. ISRIC.Google Scholar
  99. Tachie-Obeng, E., Akponikpe, P. B. I., & Adiku, S. (2013). Considering effective adaptation options to impacts of climate change for maize production in Ghana. Environmental Development, 5, 131–145.CrossRefGoogle Scholar
  100. Tetteh, F. M., & Nurudeen, A. R. (2015). Modeling site-specific fertilizer recommendations for maize production in the Sudan savannah agro-ecology of Ghana. African Journal of Agricultural Research, 10(11), 1136–1141.Google Scholar
  101. Thornton, P. K., Jones, P. G., Alagarswamy, G., & Adresen, J. (2009). Spatial variation of crop yield response to climate change in East Africa. Global Environmental Change, 19, 54–65.CrossRefGoogle Scholar
  102. Tittonell, P., Corbeels, M., van Wijk, M. T., Vanlauwe, B., & Giller, K. E. (2008a). Combining organic and mineral fertilizers for integrated soil fertility management in smallholder farming systems of Kenya: Explorations using the Crop-Soil model FIELD. Agronomy Journal, 100(5), 1511–1526.CrossRefGoogle Scholar
  103. Tittonell, P., Vanlauwe, B., Corbeels, M., & Giller, K. E. (2008b). Yield gaps, nutrient use efficiencies and response to fertilizers by maize across heterogeneous smallholder farms of western Kenya. Plant and Soil, 313, 19–37.CrossRefGoogle Scholar
  104. Tsubo, M., Walker, S., & Ogindo, H. O. (2005). A simulation model of cereal–legume intercropping systems for semi-arid regions II Model application. Field Crops Research, 93, 23–33.CrossRefGoogle Scholar
  105. Van Bavel, C. H. M. (1953). A drought criterion and its application in evaluating drought incidence and Hazard. Agronomy Journal, 45, 167–172.CrossRefGoogle Scholar
  106. Van Diepen, C., Wolf, J., Van Keulen, H., & Rappoldt, C. (1989). WOFOST: A simulation model of crop production. Soil Use Management, 5, 16–24.CrossRefGoogle Scholar
  107. Van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis with local to global relevance—A review. Field Crops Research, 143, 4–17.CrossRefGoogle Scholar
  108. Van Keulen, H., & Breman, H. (1990). Agricultural development in the West African Sahelian region: a cure against land hunger? Agriculture, Ecosystems and Environment, 32, 177–197.CrossRefGoogle Scholar
  109. Vanlauwe, B., Kihara, J., Chivenge, P., Pypers, P., Coe, R., & Six, J. (2011). Agronomic use efficiency of N fertilizer in maize–based systems in sub–Saharan Africa within the context of integrated soil fertility management. Plant and Soil, 339, 35–50.CrossRefGoogle Scholar
  110. Voortman, R. L., & Brouwerd, A. P. J. (2004). Characterization of spatial soil variability and its effects on miller yield on Sudano Sahelian Coversnad in SW Niger. Geoderma, 121, 65–82.CrossRefGoogle Scholar
  111. Wafula, B. M. (1995). Application of crop simulation in Agricultural extension and Research in Kenya. Agricultural Systems, 49, 399–412.CrossRefGoogle Scholar
  112. Williams, R. (1983). EPIC, the erosion-productivity impact calculator, volume I model documentation. Agricultural Research Service, United States Department of Agriculture.Google Scholar
  113. Wopereis, M. C. S., Haefele, S. M., Dingkuhn, M., & Sow, A. (2003). Decision support tools for irrigated rice-based systems in the Sahel. Decision support tools for rainfed crops in the Sahel at the Plot and Regional scales. In T. E. Struif Bontkes, & M. C. S. Wopereis (Eds.), Decision support tools for smallholder Agriculture in Sub–Saharan Africa: A Practical Guide (pp. 114–126). IFDC Muscle Shoals, USA, and CTA. Wageningen, The Netherlands.Google Scholar
  114. Wopereis, M. C. S., Tame’lokpo, A., Ezui, K., Gnakpe’nou, D., Fofana, B., & Breman, H. (2006). Mineral fertilizer management of maize on farmer fields differing inorganic inputs in the West African savanna. Field Crops Research, 96, 355–362.Google Scholar
  115. Zingore, S., Murwira, H. K., Delve, R. J., & Giller, K. E. (2007). Influence of nutrient management strategies on variability of soil fertility, crop yields and nutrient balances on smallholder farms in Zimbabwe. Agriculture, Ecosystems and Environment, 119, 112–126.CrossRefGoogle Scholar
  116. Zinyengere, N., Olivier, C., Sepo, H., & Tadross, M. (2015). Crop model usefulness in drylands of southern Africa: an application of DSSAT. South African Journal of Plant and Soil, 32(2), 95–104.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dilys S. MacCarthy
    • 1
  • Job Kihara
    • 2
  • Patricia Masikati
    • 3
  • Samuel G. K. Adiku
    • 4
  1. 1.Soil and Irrigation Research CentreUniversity of GhanaKpongGhana
  2. 2.International Center for Tropical Agriculture (CIAT)NairobiKenya
  3. 3.World Agroforestry Centre, (ICRAF)LusakaZambia
  4. 4.Department of Soil ScienceUniversity of GhanaLegon, AccraGhana

Personalised recommendations