FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2084235263> ?p ?o ?g. }

• W2084235263 endingPage "13" @default.
• W2084235263 startingPage "1" @default.
• W2084235263 abstract "Despite the number of site-specific studies conducted on the effect of inorganic fertilizer and organic inputs on maize yield, information is lacking on the variation in yield risks with soils and climate in sub-Saharan Africa (SSA). Information on yield risk could give an idea of where a particular input may not work well, and thus has low potential for adoption by farmers. Using analytical methods combining features of meta-analysis, response surface regression and generalized additive models, this work synthesizes information on variation of maize yield gaps and risks with inorganic fertilizer and organic inputs in relation to soils and climate. The organic inputs included in situ green manure from sunhemp (Crotalaria spp.), velvetbean (Mucuna spp.), sesbania (Sesbania spp.), tephrosia (Tephrosia spp.) and gliricidia (Gliricidia sepium). Yield gap (YG) was defined as the difference in grain yield between maize grown using a given nutrient input and without external nutrient inputs (i.e. control) under a specific study condition. Yield risk was defined as the probability of obtaining yields lower than or equal to the control across similar conditions. Yield gaps with the recommended rate of inorganic fertilizer were significantly higher on farmers’ fields compared with research stations, while with organic inputs such differences were smaller. With inorganic fertilizer, yield risks were higher (up to 22%) on Nitosols (one of the most fertile soils), compared with Luvisols (<5% risk). With sunhemp, yield risks were higher on Ferralsols (40%), which have low resilience to degradation and low sensitivity to yield decline, compared with Cambisols (<5%), which have high resilience and low sensitivity. Yield risks with velvetbean were higher on Acrisols (35%) and Lixisols (20%), which have very low resilience and high sensitivity, compared with Nitosols and Ferralsols (<15% risk). With sesbania higher yield risks were noted on Nitosols (53%) and Acrisols (39%) compared with Luvisols (17%), Ferralsols (20%) and Lixisols (22%). Response surface regression and generalized additive models indicated that yields and yield gaps with the different inputs vary with soil clay content, elevation and mean annual precipitation on the study sites. With all inputs, yield risks were lower on sites with 20–40% soil clay content, while the effect of elevation and precipitation differed widely with the type of input. It is conclude that yield gaps and yield risks vary with the soil fertility input, the sensitivity and resilience of soils and management. This study could not confirm the superiority of any of the legume species over the other across all locations. Therefore, blanket recommendation of species should be avoided. Instead, species should be targeted in niches where they can reduce yield risks and increase adoption by small-holder farmers." @default.
• W2084235263 created "2016-06-24" @default.
• W2084235263 creator A5007377237 @default.
• W2084235263 creator A5007778155 @default.
• W2084235263 creator A5066093903 @default.
• W2084235263 creator A5066302845 @default.
• W2084235263 creator A5084563733 @default.
• W2084235263 creator A5085399940 @default.
• W2084235263 date "2010-03-01" @default.
• W2084235263 modified "2023-10-14" @default.
• W2084235263 title "Variation in maize yield gaps with plant nutrient inputs, soil type and climate across sub-Saharan Africa" @default.
• W2084235263 cites W1488574179 @default.
• W2084235263 cites W1967931875 @default.
• W2084235263 cites W1968000105 @default.
• W2084235263 cites W1969456934 @default.
• W2084235263 cites W1989416106 @default.
• W2084235263 cites W2002455139 @default.
• W2084235263 cites W2010215593 @default.
• W2084235263 cites W2015412427 @default.
• W2084235263 cites W2019369625 @default.
• W2084235263 cites W2023166644 @default.
• W2084235263 cites W2024927745 @default.
• W2084235263 cites W2039302067 @default.
• W2084235263 cites W2044023561 @default.
• W2084235263 cites W2051262786 @default.
• W2084235263 cites W2060970382 @default.
• W2084235263 cites W2071506818 @default.
• W2084235263 cites W2074572296 @default.
• W2084235263 cites W2086420919 @default.
• W2084235263 cites W2117768136 @default.
• W2084235263 cites W2124051144 @default.
• W2084235263 cites W2143293538 @default.
• W2084235263 cites W2149765056 @default.
• W2084235263 cites W2168426223 @default.
• W2084235263 cites W3022139794 @default.
• W2084235263 cites W4239600449 @default.
• W2084235263 doi "https://doi.org/10.1016/j.fcr.2009.11.014" @default.
• W2084235263 hasPublicationYear "2010" @default.
• W2084235263 type Work @default.
• W2084235263 sameAs 2084235263 @default.
• W2084235263 citedByCount "92" @default.
• W2084235263 countsByYear W20842352632012 @default.
• W2084235263 countsByYear W20842352632013 @default.
• W2084235263 countsByYear W20842352632014 @default.
• W2084235263 countsByYear W20842352632015 @default.
• W2084235263 countsByYear W20842352632016 @default.
• W2084235263 countsByYear W20842352632017 @default.
• W2084235263 countsByYear W20842352632018 @default.
• W2084235263 countsByYear W20842352632019 @default.
• W2084235263 countsByYear W20842352632020 @default.
• W2084235263 countsByYear W20842352632021 @default.
• W2084235263 countsByYear W20842352632022 @default.
• W2084235263 countsByYear W20842352632023 @default.
• W2084235263 crossrefType "journal-article" @default.
• W2084235263 hasAuthorship W2084235263A5007377237 @default.
• W2084235263 hasAuthorship W2084235263A5007778155 @default.
• W2084235263 hasAuthorship W2084235263A5066093903 @default.
• W2084235263 hasAuthorship W2084235263A5066302845 @default.
• W2084235263 hasAuthorship W2084235263A5084563733 @default.
• W2084235263 hasAuthorship W2084235263A5085399940 @default.
• W2084235263 hasConcept C126343540 @default.
• W2084235263 hasConcept C134121241 @default.
• W2084235263 hasConcept C142796444 @default.
• W2084235263 hasConcept C159390177 @default.
• W2084235263 hasConcept C159750122 @default.
• W2084235263 hasConcept C18903297 @default.
• W2084235263 hasConcept C191897082 @default.
• W2084235263 hasConcept C192562407 @default.
• W2084235263 hasConcept C2777214192 @default.
• W2084235263 hasConcept C2777575642 @default.
• W2084235263 hasConcept C2778268778 @default.
• W2084235263 hasConcept C2778361644 @default.
• W2084235263 hasConcept C2780560099 @default.
• W2084235263 hasConcept C33923547 @default.
• W2084235263 hasConcept C38774213 @default.
• W2084235263 hasConcept C39432304 @default.
• W2084235263 hasConcept C6557445 @default.
• W2084235263 hasConcept C86803240 @default.
• W2084235263 hasConceptScore W2084235263C126343540 @default.
• W2084235263 hasConceptScore W2084235263C134121241 @default.
• W2084235263 hasConceptScore W2084235263C142796444 @default.
• W2084235263 hasConceptScore W2084235263C159390177 @default.
• W2084235263 hasConceptScore W2084235263C159750122 @default.
• W2084235263 hasConceptScore W2084235263C18903297 @default.
• W2084235263 hasConceptScore W2084235263C191897082 @default.
• W2084235263 hasConceptScore W2084235263C192562407 @default.
• W2084235263 hasConceptScore W2084235263C2777214192 @default.
• W2084235263 hasConceptScore W2084235263C2777575642 @default.
• W2084235263 hasConceptScore W2084235263C2778268778 @default.
• W2084235263 hasConceptScore W2084235263C2778361644 @default.
• W2084235263 hasConceptScore W2084235263C2780560099 @default.
• W2084235263 hasConceptScore W2084235263C33923547 @default.
• W2084235263 hasConceptScore W2084235263C38774213 @default.
• W2084235263 hasConceptScore W2084235263C39432304 @default.
• W2084235263 hasConceptScore W2084235263C6557445 @default.
• W2084235263 hasConceptScore W2084235263C86803240 @default.
• W2084235263 hasIssue "1-2" @default.
• W2084235263 hasLocation W20842352631 @default.

• next