Effect of Cropping System and Nitrogen on Maize and Soy Bean Yields in Western Kenya
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".