Post-harvest management and post-harvest losses of cereals in Ethiopia
Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-h...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hengsdijk, H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Food security - Dordrecht [u.a.] : Springer Netherlands, 2009, 9(2017), 5 vom: 15. Sept., Seite 945-958 |
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| Übergeordnetes Werk: |
volume:9 ; year:2017 ; number:5 ; day:15 ; month:09 ; pages:945-958 |
| Links: |
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| DOI / URN: |
10.1007/s12571-017-0714-y |
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SPR026151073 |
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| 520 | |a Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. | ||
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10.1007/s12571-017-0714-y doi (DE-627)SPR026151073 (SPR)s12571-017-0714-y-e DE-627 ger DE-627 rakwb eng Hengsdijk, H. verfasserin (orcid)0000-0002-9288-0118 aut Post-harvest management and post-harvest losses of cereals in Ethiopia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. Food loss (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Food storage (dpeaa)DE-He213 Maize (dpeaa)DE-He213 LSMS-ISA (dpeaa)DE-He213 de Boer, W. J. aut Enthalten in Food security Dordrecht [u.a.] : Springer Netherlands, 2009 9(2017), 5 vom: 15. Sept., Seite 945-958 (DE-627)595710026 (DE-600)2486755-X 1876-4525 nnns volume:9 year:2017 number:5 day:15 month:09 pages:945-958 https://dx.doi.org/10.1007/s12571-017-0714-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2017 5 15 09 945-958 |
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10.1007/s12571-017-0714-y doi (DE-627)SPR026151073 (SPR)s12571-017-0714-y-e DE-627 ger DE-627 rakwb eng Hengsdijk, H. verfasserin (orcid)0000-0002-9288-0118 aut Post-harvest management and post-harvest losses of cereals in Ethiopia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. Food loss (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Food storage (dpeaa)DE-He213 Maize (dpeaa)DE-He213 LSMS-ISA (dpeaa)DE-He213 de Boer, W. J. aut Enthalten in Food security Dordrecht [u.a.] : Springer Netherlands, 2009 9(2017), 5 vom: 15. Sept., Seite 945-958 (DE-627)595710026 (DE-600)2486755-X 1876-4525 nnns volume:9 year:2017 number:5 day:15 month:09 pages:945-958 https://dx.doi.org/10.1007/s12571-017-0714-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2017 5 15 09 945-958 |
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10.1007/s12571-017-0714-y doi (DE-627)SPR026151073 (SPR)s12571-017-0714-y-e DE-627 ger DE-627 rakwb eng Hengsdijk, H. verfasserin (orcid)0000-0002-9288-0118 aut Post-harvest management and post-harvest losses of cereals in Ethiopia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. Food loss (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Food storage (dpeaa)DE-He213 Maize (dpeaa)DE-He213 LSMS-ISA (dpeaa)DE-He213 de Boer, W. J. aut Enthalten in Food security Dordrecht [u.a.] : Springer Netherlands, 2009 9(2017), 5 vom: 15. Sept., Seite 945-958 (DE-627)595710026 (DE-600)2486755-X 1876-4525 nnns volume:9 year:2017 number:5 day:15 month:09 pages:945-958 https://dx.doi.org/10.1007/s12571-017-0714-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2017 5 15 09 945-958 |
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10.1007/s12571-017-0714-y doi (DE-627)SPR026151073 (SPR)s12571-017-0714-y-e DE-627 ger DE-627 rakwb eng Hengsdijk, H. verfasserin (orcid)0000-0002-9288-0118 aut Post-harvest management and post-harvest losses of cereals in Ethiopia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. Food loss (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Food storage (dpeaa)DE-He213 Maize (dpeaa)DE-He213 LSMS-ISA (dpeaa)DE-He213 de Boer, W. J. aut Enthalten in Food security Dordrecht [u.a.] : Springer Netherlands, 2009 9(2017), 5 vom: 15. Sept., Seite 945-958 (DE-627)595710026 (DE-600)2486755-X 1876-4525 nnns volume:9 year:2017 number:5 day:15 month:09 pages:945-958 https://dx.doi.org/10.1007/s12571-017-0714-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2017 5 15 09 945-958 |
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10.1007/s12571-017-0714-y doi (DE-627)SPR026151073 (SPR)s12571-017-0714-y-e DE-627 ger DE-627 rakwb eng Hengsdijk, H. verfasserin (orcid)0000-0002-9288-0118 aut Post-harvest management and post-harvest losses of cereals in Ethiopia 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. Food loss (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Food storage (dpeaa)DE-He213 Maize (dpeaa)DE-He213 LSMS-ISA (dpeaa)DE-He213 de Boer, W. J. aut Enthalten in Food security Dordrecht [u.a.] : Springer Netherlands, 2009 9(2017), 5 vom: 15. Sept., Seite 945-958 (DE-627)595710026 (DE-600)2486755-X 1876-4525 nnns volume:9 year:2017 number:5 day:15 month:09 pages:945-958 https://dx.doi.org/10.1007/s12571-017-0714-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2017 5 15 09 945-958 |
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Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. 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post-harvest management and post-harvest losses of cereals in ethiopia |
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Post-harvest management and post-harvest losses of cereals in Ethiopia |
| abstract |
Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. © The Author(s) 2017 |
| abstractGer |
Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. © The Author(s) 2017 |
| abstract_unstemmed |
Abstract Recent and systematic evidence on the magnitude of post-harvest losses in sub-Saharan Africa is scarce, hindering the identification of interventions to reduce losses. Here, we unlock standardized and systematically collected information on post-harvest management and farmer-reported post-harvest loss estimates from the Living Standards Measurement Study – Integrated Surveys in Agriculture. Using the data from Ethiopia, the objective is to disentangle factors that induce or relate to post-harvest losses in cereals. The data of approximately 2500 households and 5500 cereal records were analysed. Cereal post-harvest loss was reported by only 10% of these households. The average self-reported post-harvest loss was 24%. Rodents and other pests were most frequently reported to cause these losses. Adoption of improved storage methods was limited and most cereals were stored inside the house in bags. Random Forests (RF) was applied to gain insight into factors and conditions favouring post-harvest losses. Application of RF explained 31% of the variation in post-harvest losses reported by farmers. Three major factors associated with post-harvest losses were the distance of the household dwelling to the nearest market, the distance of the household dwelling to the main road, and average annual rainfall. Losses increased the further households were located from a market or main road, and losses also tended to decrease with higher rainfall. The standardized and nationally representative survey data from Ethiopia used were a good starting point for modelling post-harvest losses but the finally available information appeared to be partial. Therefore, this paper calls for better data collection, which could help to better target interventions needed to reduce post-harvest losses. © The Author(s) 2017 |
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Post-harvest management and post-harvest losses of cereals in Ethiopia |
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https://dx.doi.org/10.1007/s12571-017-0714-y |
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de Boer, W. J. |
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10.1007/s12571-017-0714-y |
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2024-07-03T19:10:06.866Z |
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| score |
7.3997984 |