Nutrient Cycling in Agroecosystems

, Volume 107, Issue 2, pp 175–186 | Cite as

Maize-nutrient response information applied across Sub-Saharan Africa

  • Charles S. Wortmann
  • Maribeth Milner
  • Kayuki C. Kaizzi
  • Maman Nouri
  • Athanase R. Cyamweshi
  • Mohammed K. Dicko
  • Catherine N. Kibunja
  • Martin Macharia
  • Ricardo Maria
  • Patson C. Nalivata
  • Negash Demissie
  • Davy Nkonde
  • Korodjouma Ouattara
  • Catherine J. Senkoro
  • Bitrus Dawi Tarfa
  • Francis M. Tetteh
Original Article


The profit potential for a given investment in fertilizer use can be estimated using representative crop nutrient response functions. Where response data is scarce, determination of representative response functions can be strengthened by using results from homologous crop growing conditions. Maize (Zea mays L.) nutrient response functions were selected from the Optimization of Fertilizer Recommendations in Africa (OFRA) database of 5500 georeferenced response functions determined from field research conducted in Sub-Saharan Africa. Three methods for defining inference domains for selection of response functions were compared. Use of the OFRA Inference Tool (OFRA-IT; resulted in greater specificity of maize N, P, and K response functions with higher R2 values indicating superiority compared with using the Harvest Choice Agroecological Zones (HC-AEZ) and the recommendation domains of the Global Yield Gap Atlas project (GYGA-RD). The OFRA-IT queries three soil properties in addition to climate-related properties while the latter two options use climate properties only. The OFRA-IT was generally insensitive to changes in criteria ranges of 20–25% used in queries suggesting value in using wider criteria ranges compared with the default for information scarce crop nutrient response functions.


Agroecological zones Data queries Extrapolation Harvest Choice Fertilizer use Optimization Recommendation domains Smallholder 



Africa soil information service


Aridity index


Coefficient of variability


Recommendation (or climate) domains of the Global Yield Gap Atlas project


Harvest Choice agroecological zone


Optimizing fertilizer recommendations in africa project


OFRA Inference Tool


Soil organic C


Sub-Saharan Africa


Temperature seasonality



OFRA is a partnership of 13 African countries, funded by the Alliance for a Green Revolution in Africa (AGRA), managed by CAB International and implemented with technical and scientific advisory support from the University of Nebraska-Lincoln to enable great farmer profitability from fertilizer use. Co-authors led in-country activities with, on average, three to four research teams per country participating. We acknowledge the contributions of these teams, of the many researchers of recent decades whose research findings were integrated into the OFRA database of response functions, and the farmers who cooperated in conducting field trials.


  1. Aiken RM, Thomas V, Waltman W (2001) Agricultural farm analysis and comparison tool (AgriFACTs). Regional Institute Online Publishing. Retrieved from
  2. HarvestChoice (2010) Agro-ecological zones of sub-Saharan Africa. International Food Policy Research Institute, Washington DC, and University of Minnesota, St. Pail MN. Available online at
  3. Hengl T, de Jesus JM, MacMillan RA, Batjes NH, Heuvelink GBM, Ribeiro E, Samuel-Rosa A, Kempen B, Leenaars JGB, Walsh MG, Gonzalez MR (2014) SoilGrids1 km—global soil information based on automated mapping. PLos ONE. doi: 10.1371/journal.pone.0105992 PubMedPubMedCentralGoogle Scholar
  4. Hengl T, Heuvelink GMB, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, Sila A, MacMillan RA, de Jesus JM, Tamene L (2015) Mapping soil properties of Africa at 250 m resolution: random forests significantly improve current predictions. PLoS ONE. doi: 10.1371/journal.pone.0125814 PubMedPubMedCentralGoogle Scholar
  5. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  6. Kaizzi CK, Byalebeka J, Semalulu O, Alou I, Zimwanguyizza W, Nansamba A, Musinguzi P, Ebanyat P, Hyuha T, Wortmann CS (2012) Maize response to fertilizer and nitrogen use efficiency in Uganda. Agron J 104:73–82CrossRefGoogle Scholar
  7. Kaizzi CK, Wortmann C, Jansen J (2013) More profitable fertilizer use for poor farmers. Better Crops 97(3):4–6Google Scholar
  8. Kaizzi KC, Mohammed MB, Nouri M (2017) Fertilizer Use optimization: principles and approach. In: Wortmann CS, Sones K (eds) Fertilizer Use Optimization in sub-Saharan Africa. 17 chapters. Published by CABI, WallingfordGoogle Scholar
  9. Lehner B, Verdin K and Jarvis A (2008) New global hydrography derived from spaceborne elevation data. Eos Trans AGU 89:93-94.
  10. Van Wart J, Van Bussel LGJ, Wolf J, Licker L, Grassini P, Nelson A, Boogaard H, Gerber J, Mueller ND, Claessens L, Van Ittersum MK, Cassman KG (2013) Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res 143:44–55CrossRefGoogle Scholar
  11. Wortmann CS, Milner M (2015) OFRA Inference Tool: an ArcGIS script tool for geo-spatial agronomic query. University of Nebraska, Lincoln, USA.
  12. Wortmann C, Grassini P, Elmore RW (2016) Optimizing maize-based cropping systems: sustainability, good agricultural practices (GAP) and yield goals. In Watson D (ed) Achieving sustainable cultivation of maize. Volume 2: cultivation techniques, pest and disease control. Burleigh Dodds Science Publishing, Cambridge, UK.
  13. Wortmann CS, Milner MA, Tesfahunegn GB (2017) Spatial analysis for optimization of fertilizer use. In: Wortmann CS, Sones K (eds) Fertilizer Use Optimization in Sub-Saharan Africa. 17 chapters. Published by CAB International, WallingfordGoogle Scholar
  14. Zomer RJ, Bossio DA, Trabucco A, Yuanjie L, Gupta DC, Singh VP (2007) Trees and water: smallholder agroforestry on irrigated lands in northern India. Colombo, Sri Lanka: International Water Management Institute. pp 45. IWMI Research Report 122
  15. Zomer RJ, Trabucco A, Bossio DA, van Straaten O, Verchot LV (2008) Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric Ecosyst Environ 126:67–80CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Charles S. Wortmann
    • 1
  • Maribeth Milner
    • 1
  • Kayuki C. Kaizzi
    • 2
  • Maman Nouri
    • 3
  • Athanase R. Cyamweshi
    • 4
  • Mohammed K. Dicko
    • 5
  • Catherine N. Kibunja
    • 7
  • Martin Macharia
    • 8
  • Ricardo Maria
    • 9
  • Patson C. Nalivata
    • 10
  • Negash Demissie
    • 11
  • Davy Nkonde
    • 6
  • Korodjouma Ouattara
    • 12
  • Catherine J. Senkoro
    • 13
  • Bitrus Dawi Tarfa
    • 14
  • Francis M. Tetteh
    • 15
  1. 1.Department of Agronomy and HorticultureUniversity of Nebraska-LincolnLincolnUSA
  2. 2.National Agricultural Research LaboratoriesKampalaUganda
  3. 3.Institut National de Recherche Agronomique du Niger (INRAN)MaradiNiger
  4. 4.Rwanda Agriculture BoardKigaliRwanda
  5. 5.Institut d’Economie RuraleBamakoMali
  6. 6.Zambia Agriculture Research Institute (ZARI)LusakaZambia
  7. 7.KALRO-KabeteNairobiKenya
  8. 8.CAB InternationalNairobiKenya
  9. 9.Instituto de Investigação Agrária de Moçambique (IIAM)MaputoMozambique
  10. 10.Lilongwe University of Agriculture and Natural ResourcesLilongweMalawi
  11. 11.Ethiopian Institute of Agricultural ResearchAddis AbabaEthiopia
  12. 12.Institut de l’Environnement et de Recherches Agricoles (INERA)OuagadougouBurkina Faso
  13. 13.Mlingano Agricultural Research InstituteTangaTanzania
  14. 14.Department of Soil Science, Faculty of Agriculture/Institute for Agricultural ResearchAhmadu Bello UniversityZariaNigeria
  15. 15.CSIR-Soil Research InstituteKwadaso-KumasiGhana

Personalised recommendations