From observation to information: data-driven understanding of on farm yield variation
Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2016
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/72485 |
| _version_ | 1855518935145775104 |
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| author | Jiménez, Daniel Dorado, Hugo Andres Cock, James H. Prager, Steven D. Delerce, Sylvain Jean Grillon, Alexandre Andrade Bejarano, Mercedes Benavides, Hector Jarvis, Andy |
| author_browse | Andrade Bejarano, Mercedes Benavides, Hector Cock, James H. Delerce, Sylvain Jean Dorado, Hugo Andres Grillon, Alexandre Jarvis, Andy Jiménez, Daniel Prager, Steven D. |
| author_facet | Jiménez, Daniel Dorado, Hugo Andres Cock, James H. Prager, Steven D. Delerce, Sylvain Jean Grillon, Alexandre Andrade Bejarano, Mercedes Benavides, Hector Jarvis, Andy |
| author_sort | Jiménez, Daniel |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers’ experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multistage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers’ fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations
confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed. |
| format | Journal Article |
| id | CGSpace72485 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace724852025-04-17T08:26:12Z From observation to information: data-driven understanding of on farm yield variation Jiménez, Daniel Dorado, Hugo Andres Cock, James H. Prager, Steven D. Delerce, Sylvain Jean Grillon, Alexandre Andrade Bejarano, Mercedes Benavides, Hector Jarvis, Andy farms planting intercropping data management soil analysis crop management agronomy explotaciones agrarias plantación cultivo intercalado gestión de datos análisis del suelo manejo de cultivo agronomía Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers’ experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multistage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers’ fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed. 2016-03-01 2016-03-07T20:36:35Z 2016-03-07T20:36:35Z Journal Article https://hdl.handle.net/10568/72485 en Open Access Public Library of Science Jiménez, Daniel; Dorado, Hugo; Cock, James; Prager, Steven D.; Delerce, Sylvain; Grillon, Alexandre; Andrade Bejarano, Mercedes; Benavides, Hector; Jarvis, Andy. 2016. From observation to information: data-driven understanding of on farm yield variation . PLoS One 11(3): e0150015. |
| spellingShingle | farms planting intercropping data management soil analysis crop management agronomy explotaciones agrarias plantación cultivo intercalado gestión de datos análisis del suelo manejo de cultivo agronomía Jiménez, Daniel Dorado, Hugo Andres Cock, James H. Prager, Steven D. Delerce, Sylvain Jean Grillon, Alexandre Andrade Bejarano, Mercedes Benavides, Hector Jarvis, Andy From observation to information: data-driven understanding of on farm yield variation |
| title | From observation to information: data-driven understanding of on farm yield variation |
| title_full | From observation to information: data-driven understanding of on farm yield variation |
| title_fullStr | From observation to information: data-driven understanding of on farm yield variation |
| title_full_unstemmed | From observation to information: data-driven understanding of on farm yield variation |
| title_short | From observation to information: data-driven understanding of on farm yield variation |
| title_sort | from observation to information data driven understanding of on farm yield variation |
| topic | farms planting intercropping data management soil analysis crop management agronomy explotaciones agrarias plantación cultivo intercalado gestión de datos análisis del suelo manejo de cultivo agronomía |
| url | https://hdl.handle.net/10568/72485 |
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