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Soil data importance in guiding maize intensification and yield gap estimations in East Africa

By: Material type: ArticleLanguage: English Publication details: United Kingdom : Taylor & Francis, 2019.ISSN:
  • 0365-0340
  • 1476-3567 (Online)
Subject(s): In: Archives of Agronomy and Soil Science United Kingdom : Taylor & Francis, 2019. v. 65, no. 6, p. 809-821Summary: Wide maize yield gaps have been reported in Eastern Africa, hence possibility for increasing production. Previous yield gap studies relied on generic soils data such as Harmonized World Soils Database (HWSD). Using CERES-Maize model, the importance of newly available and detailed Africa Soil and Information Service (AFSIS) data in estimating yield gaps and assessing intensification potentials was studied at Sidindi, Kenya and Mbinga, Tanzania. Predicted water-limited yields (Yw) at Sidindi using AFSIS and HWSD soils data were 9.21 Mg ha−1 and 9.88 Mg ha−1 (p = 0.002); and at Mbinga 10.48 Mg ha−1 and 10.90 Mg ha−1 (p = 0.085). Adequate rainfall masks differences in simulated Yw. The calibrated model predicted grain yield with a root mean square (RMSE) of 1.7 Mg ha−1 at Sidindi; and 2.13 Mg ha−1 at Mbinga. The model was sensitive to available phosphorus, with a 15% increase resulting in yield increases of 177% for treatment NK and 46% for the control. For stable organic carbon content, a 15% decrease increased grain yields for treatment PK by 57.6%. To guide intensification and yield gap estimations, accurate active soil carbon, total carbon, available phosphorus and texture data are vital.
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Wide maize yield gaps have been reported in Eastern Africa, hence possibility for increasing production. Previous yield gap studies relied on generic soils data such as Harmonized World Soils Database (HWSD). Using CERES-Maize model, the importance of newly available and detailed Africa Soil and Information Service (AFSIS) data in estimating yield gaps and assessing intensification potentials was studied at Sidindi, Kenya and Mbinga, Tanzania. Predicted water-limited yields (Yw) at Sidindi using AFSIS and HWSD soils data were 9.21 Mg ha−1 and 9.88 Mg ha−1 (p = 0.002); and at Mbinga 10.48 Mg ha−1 and 10.90 Mg ha−1 (p = 0.085). Adequate rainfall masks differences in simulated Yw. The calibrated model predicted grain yield with a root mean square (RMSE) of 1.7 Mg ha−1 at Sidindi; and 2.13 Mg ha−1 at Mbinga. The model was sensitive to available phosphorus, with a 15% increase resulting in yield increases of 177% for treatment NK and 46% for the control. For stable organic carbon content, a 15% decrease increased grain yields for treatment PK by 57.6%. To guide intensification and yield gap estimations, accurate active soil carbon, total carbon, available phosphorus and texture data are vital.

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