| 000 | 03484nam a22003857a 4500 | ||
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| 001 | G91277 | ||
| 003 | MX-TxCIM | ||
| 005 | 20231114204208.0 | ||
| 008 | 121211s ||||f| 0 p|p||0|| | | ||
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 090 | _aCIS-5437 | ||
| 100 | 1 |
_aRutto, E. _920708 |
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| 245 | 1 | 0 |
_aParticipatory evaluation of integrated pest and soil fertility management options : _bUsing ordered categorical data analysis. |
| 260 |
_aGold Coast (Australia) : _bInternational Association of Agricultural Economics, _c2006. |
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| 300 | _a23 pages | ||
| 340 | _aPrinted | ||
| 520 | _aDuring participatory rural appraisals, farmers at the Lake Victoria basin of Kenya and Uganda identified Striga, stemborer and declining soil fertility as three major constraints to maize production To reduce food insecurity, several innovative integrated technologies to address these constraints have been developed, including push-pull (maize intercropped with Desmodium and surrounded by napier grass), maize-soybean and maize-crotalaria rotations, and Imazapyrresistant (IR) maize seed coated with the herbicide. To let farmers evaluate the new technologies, 12 demonstration trials, comparing the different technologies, were established in four villages in Siaya and Vihiga districts (Western Kenya) and two villages in Busia (Uganda). These evaluations, where farmers’ appreciation and feedback on the technology are captured, are an important step in technology development. During field days at the end of short rainy seasons of 2003 and 2004, 504 farmers individually observed and rated each treatment under the different cropping systems, with and without IR maize, and with and without fertilizer, with a maize continuous monocrop as control. Farmers scored each of the 16 treatments on an ordered scale of five categories: very poor, poor, average, good, and very good. The treatments were scored for each of the criteria farmers has previously determined (including yield, resistance to Striga and stemborer, and improvement of soil fertility). Analysis of the evaluation, using ordinal regression, show significant differences in farmers’ preference by year and site. There was, however, little effect of farm and farmer characteristics such as farm size and gender of the observer. Ordinal regression of farmers’ scores are not as intuitive and also bit cumbersome to use, but they have a better theoretical foundation than other methods, in particular the use of means. This paper shows how the method can be used, and concludes that, with some effort, it is a convenient way to analyse farmers’ ranking of a large number of options. | ||
| 536 | _aConservation Agriculture Program|Socioeconomics Program | ||
| 546 | _aText in English | ||
| 591 | _a0902|Berta | ||
| 594 | _aINT2512|INT2340 | ||
| 595 | _aCSC | ||
| 650 | 7 |
_2AGROVOC _91654 _aFarmers |
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| 650 | 7 |
_2AGROVOC _91988 _aTechnology |
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| 650 | 7 |
_2AGROVOC _91952 _aSoil fertility |
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| 650 | 7 |
_2AGROVOC _94371 _aData analysis |
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| 700 | 1 |
_aDe Groote, H. _gFormerly Socioeconomics Program _gFormerly Sustainable Agrifood Systems _8INT2512 _9841 |
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| 700 | 1 |
_91967 _aVanlauwe, B. |
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| 700 | 1 |
_aKanampiu, F. _9546 |
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| 700 | 1 |
_aOdhiambo, G.D., _9628 |
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| 700 | 1 |
_aKhan, Z.R. _914798 |
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| 773 |
_dGold Coast (Australia) : International Association of Agricultural Economics, 2006. _tConference of the International Association of Agricultural Economics; Gold Coast (Australia); 12-18 Ago 2006 |
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| 942 |
_cPRO _2ddc |
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| 999 |
_c6236 _d6236 |
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