Yield, carbon stock, and price dynamics of agroforestry tree species in district Mardan, Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
A socio-economic study was conducted in district Mardan of the Khyber Pakhtunkhwa (KP) province of Pakistan to get a comprehensive knowledge of the agroforestry tree species grown on the farmlands, their yield, and carbon stock. For yield and carbon stock estimation, data were collected from 59 sample plots by measuring the diameter, height, volume, and biomass of selected agroforestry tree species through D-tape and Haga altimeter. A total of 59 sample plots were inventoried using 2.5 percent sampling intensity. Each sample plot has an area of 0.5 ha, where each tree with a Diameter at Breast Height (DBH) ≥ 5 cm was inventoried. The calculated amount of volume of each tree species was then converted to biomass by multiplying it by the density of wood and the Biomass Expansion Factor (BEF). Total yield and C stock for the selected agroforestry tree species were 11535.2 metric tons and 2102.2 metric tons, respectively. Populus euroamericana is classified as the main tree with 28% growing stock prior to Morus alba by 21%, while Melia azedarach, Eucalyptus camaldulensis, Dalbergia sissoo, Acacia nilotica, Salix tetrasperma, and Bombax ceiba consist of 15%, 12%, 8%, 6%,7% and 3% growing stock respectively. Among the species found in different sampling plots the yield of Populus euroamericana was found to be 4747.5 metric tons and it was followed by the species Morus alba found at 2027.3 metric tons. Similarly, the volume for Melia azedarach, Eucalyptus camaldulensis, Dalbergia sissoo, Salix spp, Boombox ceiba, and Acacia nilotica was 1532.2 tons,1503 ton,745.7,203.5ton, 555.4ton and 220.5ton, respectively. The carbon stock for Populus euroamericana was calculated as 777.8 ton/ha, while for Eucalyptus camaldulensis, Melia azedarach, Morus alba, Dalbergia sissoo, Acacia nilotica, Salix species, and Bombax ceiba it was calculated as 312.3ton/ha, 272.1ton/ha, 363ton/ha, 245.1ton/ha, 51.4ton/ha, 27.3ton/ha and 53.2ton/ha, respectively. The questionnaire survey conducted for price dynamics showed that the majority of respondents purchase timber from the market for construction. But they use farm trees with low-quality city construction. They dislike using local timber in the conventional building as timber from farm trees is liable to insect attack. Rs. 50,000-100000, (33.33%) of daily sales was concluded from 50% of the trader while (16.7%) of the traders have their sales between Rs.150,000-200,000. Therefore, it is concluded by the authors that both provincial and federal government should promote agroforestry in Pakistan through different incentives because it has the potential to cope with dilemma of deforestation of natural forests and improve the livelihood of local peoples. It is strongly recommended that special projects just like the Ten Billion Tree Afforestation Project (T-BTTP) should be launched for agroforestry plantation and promotion in the country to sustain the ecological harmony and uplift the socio-economic condition of the peoples of Pakistan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".