Amid the epoch of global overpopulation, the agroforestry system can intervene as a novel practice that can safeguard agricultural sustainability, provide a means of livelihood, yield ecological benefits, and contribute to household food security. However, the adoption of sustained agroforestry practices requires an understanding of both farmers’ personal characteristics and perceived statuses, constituting a difficult task to anticipate, analyze, and visualize. To this end, it is crucial to understand and identify the most significant factors driving the adoption of agroforestry. This research attempts to examine the determinants and psychological drivers of smallholder farmers’ intention to plant trees on farmland and the factors that may discourage them from doing so. The conceptual framework of the study was developed based on the theory of planned behavior. We draw on survey data from 400 smallholder farmers in a northern irrigated plain of Pakistan. A binary logistic regression model was employed to explore how socio-psychological drivers influence farmers’ decision to adopt agroforestry practices. The study results reveal that 60.5% of the respondents prefer to adopt agroforestry practices. The results from a logit estimation showed that socio-economic variables such as family size, land ownership, subsidies, livestock rearing, sources of energy, and total income had a significant positive influence on the planting of trees on farmland, while age had a negative influence. Moreover, runoff control and the control of heat significantly affect the perceptions of farmers regarding the adoption of agroforestry. The findings suggest that implementing policies that enhance the delivery of robust agricultural extension services and training programs for farmers could disseminate the agroforestry system countrywide, which might offer substantial benefits for farmers in the long term while maintaining environmental integrity.

Estimation of plant community composition, aboveground biomass and carbon stock is crucial for understanding forest ecology, strengthening environmental management, and developing effective tools and policies for forest restoration. This study was conducted in nine different forest reserves in Sri Lanka from 2012 to 2018 to examine the impact of community-based forest restoration (CBFR) on stand structural attributes, aboveground biomass, and carbon stock compared to state-managed forests. In total, 180 plots (90 plots in community-managed restoration blocks (CMRBs) and 90 plots assigned to state-managed restoration blocks (SMRBs)) were sampled at the study site. To conduct an inventory of standing trees, circular plots with a radius of 12.6 m (equivalent to an area of 500 square meters) were established. The Shannon diversity index, Allometric equations and Difference in Differences (DID) estimation were used to assess the data. Our study provides evidence of the positive impact of the CBFR program on enriching trees diversity. Considering stand structural attributes of both blocks showed higher trees density in the smaller diameter at breast height (DBH) category, indicating growth in both CMRBs and SMRBs. The results showed that tree biomass and carbon density were disproportionally distributed across the nine different forest reserves. On average, tree biomass and carbon density were higher in SMRBs (79.97 Mg ha⁻¹, 37.58 Mg C ha⁻¹) compared to CMRBs (33.51 Mg ha⁻¹, 15.74 Mg C ha⁻¹). However, CMRBs in Madigala reserve represent the highest biomass (56.53 and 59.92 Mg ha⁻¹) and carbon density (26.57 and 28.16 Mg C ha⁻¹). The results of biomass and carbon estimates were higher in all SMRBs in the nine different forest reserves compared to CMRBs. The findings suggest that future forest restoration programs in Sri Lanka should enhance participatory approaches to optimize tree species diversity, density and carbon storage, particularly in community-controlled forests. Our findings could assist developing tropical nations in understanding how CBFR impacts forest restoration objectives and improves the provision of ecological services within forests.

Confidential ledgers cannot usually be pooled across entities, yet generic private federated learning does not preserve the identities that make accounting data meaningful. This paper develops Balance-Aware Federated Analytics (BAFA), a constrained federated framework that combines voucher-level differential privacy, debit–credit and period-continuity regularization, account-hierarchy smoothing, secure aggregation, and a one-sided balance-aware update correction. The formulation defines neighboring ledgers by replacement of one complete voucher, bounds the sensitivity of the released representation, model update, and compressed balance sketch, and composes one cached representation release and all round-level aggregate releases with a Rényi differential-privacy accountant that explicitly models the minimum number of non-colluding noise contributors. It also specifies period-complete aggregation for multi-line vouchers, derives the one-sided correction from a half-space projection, and gives first-order balance-safety, hierarchy-stability, convergence, and complexity results under non-IID data, clipping, privacy noise, and sketch error. Our experiments use PaySim, IEEE-CIS Fraud Detection, and UCI Online Retail transformed into accounting-style multi-entity ledgers. The reported points indicate that BAFA improves predictive utility and normalized balance consistency relative to private federated baselines while keeping membership-inference attack AUC near random guessing. The transformed-ledger evaluation is intended as controlled evidence; validation on native enterprise ledgers remains necessary.