Enhancing Indigenous Cropping Systems under Climate Change: A Case Study of Maize (Zea mays L.) and Groundnut (Arachis hypogea) in Northern Ghana
Bibliographic record
Abstract
Due to the continuous cropping of maize and groundnuts on the same land in northern Ghana for livelihood, production yields have declined significantly below potential levels. The purpose of this study was to evaluate the ecological limitations affecting maize and groundnut productivity in northern Ghana and also to analyze the socioeconomic constraints impacting the livelihoods of smallholder farmers. Five cropping systems (viz. sole continuous maize (SCM), sole continuous groundnut (SCG), maize-groundnut intercrop (MGI), groundnut/maize rotation (GMR) and maize/groundnut rotation (MGR)), each with or without fertilizer were established under RCBD at Nyankpala during the 2021 and 2022 cropping seasons. For the fertilized rotation treatments, the maize crop received 60-N, 40-P2O5, and 40-K2O, kg/ha, while the groundnut crop received 20-N, 40-P2O5, and 40-K2O, kg/ha using NPK 11, 22, 21, kg/ha with trace elements S, Zn and B. The cropping systems were characterised on grain yields, yield related parameters, resource use and economic returns on investments. The results showed that intercrop and rotation treatments gave better yields. Their land equivalent ratios (LER) were 1.2 and 1.09 respectively, in the two seasons. Maize grain yield under the rotation increased from 2.5 to 3.8 t/ha while groundnut pod yield increased from 0.6 to 0.9 t/ha. The system with highest economic returns was recorded for the rotation systems either with or without fertilizer application. To improve livelihoods and productivity, smallholder farmers in northern Ghana should adopt intercrop and rotation systems, incorporate fertilizer application, and consider soil fertility management practices. Policy support and extension services can facilitate the adoption of these sustainable agricultural practices.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".