Effect of Cropping System and Nitrogen on Maize and Soy Bean Yields in Western Kenya
Notice bibliographique
Résumé
Inherent low soil fertility in the highly weathered and degraded soils largely accounts for low and unsustainable crop yields in most African countries (Okalebo et al., 2006; Sanchez et. al., 1997). Productivity of maize and soybeans in Kenya, particularly in the western region is generally low. The high costs of inorganic fertilizers and the possibility of decreasing soil productivity in due to soil mining and environmental pollution demands a reassement of their use, vis-a-vis, other alternative technologies. The objective of this study was to assess the effect of selected cropping systems, N fertilizer and manure on maize and soy bean crop yields in western Kenya (Bungoma, Vihiga, and Teso). The experiment was a split plot design arranged and laid down in a randomized complete block design (RCBD) with three replications. Cropping systems (mono cropping, MBILI and conventional) were the main factor while fertility interventions (FYM and CAN) were the treatments. The cropping systems consisted of conventional (Maize and soy beans were planted as intercrop in 37.5 cm alternating rows), MBILI (Managing Beneficial Interactions in Legume Intercrops), maize and soybean monocrop. Five fertilizer treatments were used: calcium ammonium nitrate and farm yard manure, both applied at the rate of 30 kg N ha-1 and 75 kg N ha-1, respectively, and absolute control that had no added treatment. Field experiments were planted during two cropping seasons of short rains season (August to December 2011) and one long rains season (April to August 2012) Generally, maize yields were found to be significantly higher in both the mono cropping system (mean yield: 2.00 t/ha) and MBILI system (mean yield: 1.77 t/ha) compared to conventional farming (mean yield: 1.27 t/ha). Soybean yields were found, overall, to be significantly higher in the mono cropping system (mean: 1.00t/ha) compared to both MBILI (mean: 0.75 t ha-1) and conventional system (mean: 0.59 t ha-1). For both maize and soy bean grain, application of CAN75N and planting with FYM75N were found to significantly increase yields, compared to either top dressing with CAN30N or planting with FYM30N. CAN generally produced higher yields with monocrop system whereas MBILI system did better when FYM is used, suggesting that an optimal system could consist of MBILI with organic fertilizers. The mean Land Equivalent Ratio (LER) values were always greater than 1.0 for intercropping and I.0 for mono cropping system. Manure was found to produce similar yields as those in fertilizer applications. This study recommends MBILI system and organic manure as a beneficial way of growing maize and soy beans
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».