dc.contributor.author | Munialo, Sussy | |
dc.contributor.author | Sigrun, Dahlin A | |
dc.contributor.author | Onyango, Cecilia M | |
dc.contributor.author | Oluoch-Kosura, W | |
dc.contributor.author | Marstorp, Hakan | |
dc.contributor.author | Oborn, Ingrid | |
dc.date.accessioned | 2021-08-16T07:03:40Z | |
dc.date.available | 2021-08-16T07:03:40Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Munialo S, AS D, Onyango CM, Oluoch-Kosura W, Marstorp H, Öborn I. "Soil and management-related factors contributing to maize yield gaps in western Kenya." Food and Energy Security Journal.1-17. 2019:1-7. | en_US |
dc.identifier.uri | https://www.researchgate.net/publication/337370844_Soil_and_management-related_factors_contributing_to_maize_yield_gaps_in_western_Kenya | |
dc.identifier.uri | http://erepository.uonbi.ac.ke/handle/11295/155196 | |
dc.description.abstract | The solution to reducing existing yield gaps on smallholder farms lies in understanding
factors limiting yield in areas with agricultural intensification potential.
This study applied an integrated analysis approach comprising Classification and
Regression Tree (CART), generalized linear mixed model (GLMM), and factor analysis
(FA), to explain soil and management-related factors influencing maize yield
gaps, in order to enhance yields. The study was conducted in Mukuyu and Shikomoli
in western Kenya, sites with, respectively, high and low agroecological potential
regarding soil fertility. Maize yield gaps were quantified by comparing yields on the
90th percentile of farms to yields determined in 189 fields on 70 randomly sampled
smallholdings. Soil and management-related factors were determined at early and
late maize development stages.
Maize yield on the 90th percentile of farms in Mukuyu and Shikomoli was 5.1 and 4.8 t/
ha, respectively, and the average yield gap was 1.8 and 2.6 t/ha, representing 35% and 54%
unachieved yield for Mukuyu and Shikomoli, respectively. In FA, soil was revealed to be
the main factor influencing maize yield gaps at both sites, rather than management-related
variables. The CART method identified maize density, chlorophyll values, maize height, and
depth to compact layer as consistent factors affecting yield at both sites, while GLMM identified
soil texture (silt content) as important. According to CART, weed cover at early stages
and maize density at late stages were the most limiting factor in maize production in Mukuyu
and Shikomoli, respectively. Generalized linear mixed model analysis identified agroecologyspecific
factors influencing maize yield gaps as soil-available phosphorus and zinc, plus weed
pressure at early maize stages in Mukuyu, and plus soil cation exchange capacity and exchangeable
magnesium in Shikomoli. Through an integrated approach, it was possible to identify
both consistent and agroecology-specific factors limiting crop yields. This can increase the
applicability of the findings to smallholder farms. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Soil and management-related factors contributing to maize yield gaps in western Kenya | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | critical yield periods, integrated approach, intensification potential, management, soil, yield gap | en_US |
dc.title | Soil and management-related factors contributing to maize yield gaps in western Kenya | en_US |
dc.type | Article | en_US |