World Agroforestry Centre

Agroforestry, where trees and shrubs are planted in row-alley systems, can utilize the natural ability of plants to interact with pollutants and serve as a passive biotechnological method for improving air quality. A method for programming air phytoremediation processes is presented, using appropriately shaped plant structures, considering species characteristics and the spatial configuration of plants in row-alley plantings. The main objectives of this study were: to determine the relationship between pollution reduction and the characteristics of plant communities, considering the parameters of individual plants and group characteristics, to determine strategic parameters for the interaction between plants and pollutant flows, and to identify optimization paths for each stage. The optimization of the air phytoremediation process is presented using the example of changes in the fine particulate matter (PM2.5) concentration pattern, analyzed through numerical experiments using micrometeorological computational fluid dynamics models (ENVI-met software). Ex-ante analysis of hypothetical scenarios showed that introducing appropriate configurations of variable vegetation structure could lead to pollution reductions of up to 19%. The effectiveness of the presented plant systems qualifies this method as a type of bioengineering technology, supporting the multifunctionality of agroforestry systems.

Mangrove ecosystems face numerous conservation challenges due to human-induced pressures, climate change, and natural disruptions. This paper discusses the restoration initiatives and obstacles encountered in conserving mangroves in Kilifi County, Kenya. Between 2019 and 2024, significant progress was recorded in various restoration sites across the county, with the planting of over 16 million propagules and seedlings. The main species targeted for restoration included <i>Ceriops</i><i> </i><i>tagal</i>, <i>Rhizophora</i><i> </i><i>mucronata</i>, and <i>Avicennia</i><i> </i><i>marina</i>, with efforts largely centered on mangrove rehabilitation. Restoration activities were carried out in key areas such as Kanagoni, Ngomeni, Kilifi Creek, and Mida Creek. The adoption of innovative techniques, such as enrichment planting, contributed to the overall success of these initiatives. Despite these positive outcomes, several challenges emerged. Environmental pressures, including damage by crabs and grazing animals, negatively affected seedling survival. Limited resources hindered the expansion of restoration efforts, while poor access to remote areas posed difficulties for consistent monitoring. Additionally, the lack of structured collaboration frameworks often delayed stakeholder coordination, and illegal practices such as unregulated logging continued to threaten long-term sustainability. The paper recommends establishing clear and effective collaboration frameworks that outline stakeholder roles and responsibilities. This would improve coordination, and support timely execution of projects. Strengthening partnerships with local communities is also encouraged, as their involvement in sourcing planting materials and participating in restoration activities can foster a sense of ownership and motivate sustained engagement.

Hydraulic redistribution (HR) by deep-rooting trees can provide water from deeper soil layers to shallow-rooted understory plants, yet species-specific and temporal patterns of HR water uptake remain poorly understood. We investigated whether HR water from mature oaks ( Quercus robur L./ Quercus petraea (Matt.) Liebl.) is preferentially taken up by oak seedlings compared to co-occurring understory species, and how HR water use varies over the course of the day. During drought periods in mature oak stands in Germany, two field experiments were conducted. In a deep-soil 2 H-labeling experiment of mature oak trees, HR water was detected in the roots of three different understory plants. Six days after labeling, HR water amounted to 16 ± 8% (oak), 13 ± 7% (black cherry, Prunus serotina Ehrh.), and 8 ± 4% (small balsam, Impatiens parviflora DC.) of total root water content. Although intraspecific oak–oak HR was initially the highest, after 60 days all species contained approximately 20% HR water, indicating no persistent species-specific advantage. A second experiment analyzing natural δ 18 O abundance revealed pronounced diurnal dynamics of HR water use in oak seedlings. Contrary to expectations, the HR water fraction peaked at midday, when transpiration was highest. Overall, 29 ± 6% of the daily transpired water in oak seedlings originated from HR. These results demonstrate that HR can substantially contribute to seedling water use during dry periods, highlighting the ecological importance of HR for understory plant water supply in temperate forests. Future studies should analyze specific pathways through which HR water is transported from redistributing plants to the roots of receiving plants to assess the impact of plant species, degree and type of mycorrhization or soil physical properties on HR.

Abstract Coffee agroforestry systems and natural coffee forest are generally seen as beneficial for biodiversity conservation, but this may not always be the case. While coffee agroforestry systems and natural coffee forest are widely recognized for their biodiversity conservation values, the impact of them together with households’ wealth categories on woody species diversity, composition and structure is still remain gap. This study examined the impact of coffee agroforestry systems, natural coffee forest and wealth status on woody species diversity and structure in the Yayu Coffee Forest Biosphere Reserve, Southwest Ethiopia. Three coffee systems were considered for the study, namely natural coffee forest, semi coffee forest, and homegarden coffee. In all, 90 sample plots were randomly selected and surveyed. A total of 27 woody species belongs to 17 families were recorded. All diversity indices, confirmed significantly higher biodiversity in natural coffee forest. Wealth class influenced species diversity, with richer households maintaining significantly higher species richness (p < 0.001). The general linear model showed the interactions of determinant variables had significantly positive effects on biodiversity values (p < 0.001). Structural characteristics such as DBH was significantly greater in natural forest coffee (p < 0.05). Similarly, Semi Forest coffee had significantly the highest basal area (p < 0.05). The highest stem density was recorded both at natural forest coffee and semi forest coffee with no significant difference between them (p > 0.05). Rarefaction analyses confirmed adequate sampling coverage. The findings emphasize the highest significant role of natural forest coffee and semi forest coffee among with rich wealth class for biodiversity conservation.

Abstract Dietary selenium (Se) deficiency is widespread in Malawi, due to the limited supply of Se in the predominantly maize-based food system characterised by low Se concentration. This study examined the spatial variation of Se in maize grains in Malawi, to soil properties and landscape features. Co-located soil and maize grain samples were collected in a spatially representative survey. Selenium concentration in maize, soil properties, and environmental covariates were determined. Soil and environmental variables were tested as potential predictors of Se concentration in maize. A False Discovery Rate (FDR) control was used within a Linear Mixed Model (LMM) framework. Selenium concentrations in maize ranged from below detection limits (7.69 μg kg−1) to 1852 μg kg−1 with mean and median values of 39.1 and 16.8 μg kg−1, respectively. The ranges of concentrations of Se fractions in soil were (i) soluble Se 0.181–18.8 μg kg−1 with mean and median values of 3.94 and 3.29 mg μg kg−1 respectively; (ii) adsorbed Se 0.019–119 μg kg−1 with mean and median values of 3.72 and 3.02 μg kg−1 respectively; (iii) organically bound Se 9.43–1334 μg kg−1 with mean and median values of 123 and 92.3 μg kg−1 respectively. A LMM for maize Se concentration was used for which the independent log transformed variables of soil soluble Se, adsorbed Se, oxalate extracted oxides, soluble and exchangeable sulphur had predictive value (p <0.01 in all cases, with FDR controlled at <0.05). Downscaled mean annual temperature also explained some spatial variation in grain Se concentration. Spatial variation of Se in maize showed relationships with soil and environmental variables, which can be used to identify areas most at risk of Se deficiency and thus inform policy responses. However, only a small proportion of the variation was explained indicating more analysis of Se geochemistry in soil may provide more explanatory insights.

Abstract Background Food insecurity is one of the most pressing problems confronting households in sub-Saharan Africa (SSA). It is particularly acute in low income areas across the continent. Despite Rwanda’s economic progress, food insecurity persists especially in rural areas, necessitating empirical evidence to inform targeted interventions that address the complex interplay of socio-economic, environmental and policy factors affecting household food insecurity. Factors affecting food insecurity vary, and obtaining context-specific information is necessary for designing relevant interventions. This empirical study analyzes factors affecting the probability of experiencing severe food insecurity in Eastern Rwanda and discusses how land restoration strategies like agroforestry may contribute to it. Panel data collected in 2018 and 2022 from 1100 randomly selected households are analyzed using both descriptive statistics and a correlated random effects probit model. Results The findings show a generally high level of food insecurity, with sample households having average food insecurity experience scale scores of 6.02 in 2018 and 5.73 in 2022. Moreover, 63% and 60% of the households experienced severe food insecurity in the two periods, respectively. The empirical results show that farming practices and household socio-economic characteristics played a more significant role in food insecurity status. Households that cultivated different crops had a lower probability of experiencing severe food insecurity and larger households were more likely to experience severe food insecurity. However, agroforestry-related variables were not statistically significant in reducing the probability of severe food insecurity experience in the study area. Conclusion The study concludes that food insecurity in the study area is high. To address the prevailing situation, efforts to reduce food insecurity should focus on solutions that could increase food production in the short term, such as improving household socio-economic status, diversifying crop production and market-focused production. However, these need to be aligned with local needs and ecological conditions. Agroforestry interventions should focus on integrating suitable tree species into farming systems, and future studies should account for the time dimension to accurately capture long-term effects of such interventions. Moreover, experimental studies to enable rigorous impact analysis of agroforestry interventions are recommended.

The article describes production of a high spatial resolution (30 m) bimonthly light use efficiency (LUE) based gross primary productivity (GPP) data set representing grasslands for the period 2000 to 2022. The data set is based on using reconstructed global complete consistent bimonthly Landsat archive (400TB of data), combined with 1 km MOD11A1 temperature data and 1° CERES Photosynthetically Active Radiation (PAR). First, the LUE model was implemented by taking the biome-specific productivity factor (maximum LUE parameter) as a global constant, producing a global bimonthly (uncalibrated) productivity data for the complete land mask. Second, the GPP 30 m bimonthly maps were derived for the global grassland annual predictions and calibrating the values based on the maximum LUE factor of 0.86 gCm-2d-1MJ-1. The results of validation of the produced GPP estimates based on 527 eddy covariance flux towers show an R-square between 0.48-0.71 and root mean square error (RMSE) below ~2.3 gCm-2d-1 for all land cover classes. Using a total of 92 flux towers located in grasslands, the validation of the GPP product calibrated for the grassland biome revealed an R-square between 0.51-0.70 and an RMSE smaller than ~2 gCm-2d-1. The final time-series of maps (uncalibrated and grassland GPP) are available as bimonthly (daily estimates in units of gCm-2d-1) and annual (daily average accumulated by 365 days in units of gCm-2yr-1) in Cloud-Optimized GeoTIFF (~23TB in size) as open data (CC-BY license). The recommended uses of data include: trend analysis e.g., to determine where are the largest losses in GPP and which could be an indicator of potential land degradation, crop yield mapping and for modeling GHG fluxes at finer spatial resolution. Produced maps are available via SpatioTemporal Asset Catalog (http://stac.openlandmap.org) and Google Earth Engine.