|Title||Can yield variability be explained? Integrated assessment of maize yield gaps across smallholders in Ghana|
|Author(s)||Loon, Marloes P. van; Adjei-Nsiah, Samuel; Descheemaeker, Katrien; Akotsen-Mensah, Clement; Dijk, Michiel van; Morley, Tom; Ittersum, Martin K. van; Reidsma, Pytrik|
|Source||Field Crops Research 236 (2019). - ISSN 0378-4290 - p. 132 - 144.|
Plant Production Systems
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Crop experiments - Crop modelling - Farm household survey - Integrated assessment - Smallholder farms - Yield gaps - Yield potential|
Agricultural production in Ghana should more than double to fulfil the estimated food demand in 2050, but this is a challenge as the productivity of food crops has been low, extremely variable and prone to stagnation. Yield gap estimations and explanations can help to identify the potential for intensification on existing agricultural land. However, to date most yield gap analyses had a disciplinary focus. The objective of this paper is to assess the impact of crop management, soil and household factors on maize (Zea mays) yields in two major maize growing regions in Ghana through an integrated approach. We applied a variety of complementary methods to study sites in the Brong Ahafo and Northern region. Farm household surveys, yield measurements and soil sampling were undertaken in 2015 and 2016. Water-limited potential yield (Y w ) was estimated with a crop growth simulation model, and two different on-farm demonstration experiments were carried out in 2016 and 2017. There is great potential to increase maize yields across the study sites. Estimated yield gaps ranged between 3.8 Mg ha −1 (67% of Y w ) and 13.6 Mg ha −1 (84% of Y w ). However, there was no consistency in factors affecting maize yield and yield gaps when using complementary methods. Demonstration experiments showed the potential of improved varieties, fertilizers and improved planting densities, with yields up to 9 Mg ha −1 . This was not confirmed in the analysis of the household surveys, as the large yield variation across years on the same farms impeded the disclosure of effects of management, soil and household factors. The low-input nature of the farming system and the incidence of fall armyworm led to relatively uniform and low yields across the entire population. So, farmers’ yields were determined by interacting, and strongly varying, household, soil and management factors. We found that for highly variable and complex smallholder farming systems there is a danger in drawing oversimplified conclusions based on results from a single methodological approach. Integrating household surveys, crop growth simulation modelling and demonstration experiments can add value to yield gap analysis. However, the challenge remains to improve upon this type of integrated assessment to be able to satisfactorily disentangle the interacting factors that can be managed by farmers in order to increase crop yields.