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Effect of sowing date distributions on simulation of maize yields at regional scale – A case study in Central Ghana, West Africa

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  • Srivastava, Amit Kumar
  • Mboh, Cho Miltin
  • Gaiser, Thomas
  • Webber, Heidi
  • Ewert, Frank

Abstract

In sub-Saharan Africa, with its high rainfall variability and rainfed agricultural production system of maize (Zea mays L), the estimation of its sowing date is a crucial decision for farmers. To support decision making in rainfed agriculture, different methods using “probabilistic approaches” for the selection of the sowing dates at the regional level has been developed where most of the times we only have information about the probable sowing period. The crop model LINTUL5 embedded into a general modelling framework, SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management) has been combined with a multilayer soil water balance model (SLIM) to simulate maize yields in Central Ghana. Different assumptions about the sowing date distributions at the regional level were compared to the corresponding deterministic approaches. The simulated regional maize yields with the probability-based approaches showed always the lower RMSE compared to the deterministic approaches, although significant in all cases. The approach A4-S4, where we assumed that sowing dates are normally distributed around the sowing day estimated with a rainfall based rule were the best approach in capturing the spatial and temporal variability of maize yields at the regional level. The assumption of a probabilistic distribution of sowing dates within a given sowing period tends to be superior to deterministic sowing date selection because the decisions about sowing dates are often driven by factors like availability of labor, capital or seeds and are hence much more complex than those assumed in existing crop models.

Suggested Citation

  • Srivastava, Amit Kumar & Mboh, Cho Miltin & Gaiser, Thomas & Webber, Heidi & Ewert, Frank, 2016. "Effect of sowing date distributions on simulation of maize yields at regional scale – A case study in Central Ghana, West Africa," Agricultural Systems, Elsevier, vol. 147(C), pages 10-23.
    • Handle: RePEc:eee:agisys:v:147:y:2016:i:c:p:10-23
    DOI: 10.1016/j.agsy.2016.05.012
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    References listed on IDEAS

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    1. Srivastava, Amit Kumar & Mboh, Cho Miltin & Gaiser, Thomas & Kuhn, Arnim & Ermias, Engida & Ewert, Frank, 2019. "Effect of mineral fertilizer on rain water and radiation use efficiencies for maize yield and stover biomass productivity in Ethiopia," Agricultural Systems, Elsevier, vol. 168(C), pages 88-100.
    2. Gina Lopez & Hannah Beate Kolem & Amit Kumar Srivastava & Thomas Gaiser & Frank Ewert, 2019. "A Model-Based Estimation of Resource Use Efficiencies in Maize Production in Nigeria," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    3. Ying Zhang & Jayashankar M. Swaminathan, 2020. "Improved Crop Productivity Through Optimized Planting Schedules," Manufacturing & Service Operations Management, INFORMS, vol. 22(6), pages 1165-1180, November.
    4. Ibrahim Wahab & Magnus Jirström & Ola Hall, 2020. "An Integrated Approach to Unravelling Smallholder Yield Levels: The Case of Small Family Farms, Eastern Region, Ghana," Agriculture, MDPI, vol. 10(6), pages 1-27, June.

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