Balancing economic and ecological functions in smallholder and industrial oil palm plantations.

The expansion of the oil palm industry in Indonesia has improved livelihoods in rural communities, but comes at the cost of biodiversity and ecosystem degradation. Here, we investigated ways to balance ecological and economic outcomes of oil palm cultivation. We compared a wide range of production systems, including smallholder plantations, industrialized company estates, estates with improved agronomic management, and estates with native tree enrichment. Across all management types, we assessed multiple indicators of biodiversity, ecosystem functions, management, and landscape structure to identify factors that facilitate economic-ecological win-wins, using palm yields as measure of economic performance. Although, we found that yields in industrialized estates were, on average, twice as high as those in smallholder plantations, ecological indicators displayed substantial variability across systems, regardless of yield variations, highlighting potential for economic-ecological win-wins. Reducing management intensity (e.g., mechanical weeding instead of herbicide application) did not lower yields but improved ecological outcomes at moderate costs, making it a potential measure for balancing economic and ecological demands. Additionally, maintaining forest cover in the landscape generally enhanced local biodiversity and ecosystem functioning within plantations. Enriching plantations with native trees is also a promising strategy to increase ecological value without reducing productivity. Overall, we recommend closing yield gaps in smallholder cultivation through careful intensification, whereas conventional plantations could reduce management intensity without sacrificing yield. Our study highlights various pathways to reconcile the economics and ecology of palm oil production and identifies management practices for a more sustainable future of oil palm cultivation.

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe.

The monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape's forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

A new method for selecting sites for soil sampling, coupling global weighted principal component analysis and a cost-constrained conditioned Latin hypercube algorithm.

Analysing spatial patterns of soil properties in a landscape requires a sampling strategy that adequately covers soil toposequences. In this context, we developed a hybrid methodology that couples global weighted principal component analysis (GWPCA) and cost-constrained conditioned Latin hypercube algorithm (cLHC). This methodology produce an optimized sampling stratification by analysing the local variability of the soil property, and the influence of environmental factors. The methodology captures the maximum local variances in the global auxiliary dataset with the GWPCA, and optimizes the selection of representative sampling locations for sampling with the cLHC. The methodology also suppresses the subsampling of auxiliary datasets from areas that are less representative of the soil property of interest. Consequently, the method stratifies the geographical space of interest in order to adequately represent the soil property. We present results on the tested method (R2 = 0.90 and RMSE = 0.18 m) from the Guinea savannah zone of Ghana. •It defines the local structure and accounts for localized spatial autocorrelation in explaining variability.•It suppresses the occurrence of model-selected sampling locations in areas that are less representative of the soil property of interest.

Methods of analyzing regional dermatological care as exemplified by the city of Hamburg.

BACKGROUND: The rural-urban divide is often linked to regional inequalities in healthcare. However, studies have also shown regional healthcare disparities within urban areas. To evaluate these studies, further parameters such as accessibility must be added to the standard criteria. The objective of this study was to present methodic tools for evaluating dermatological healthcare provision in Hamburg, primarily focusing on accessibility. METHODS: Analyzing data from 97 districts, the geographical distribution of 101 dermatologists and the physician-patient ratio were determined. In a second step, network analysis regarding accessibility was performed. RESULTS: There are regional inequalities in Hamburg with respect to dermatological care. Depending on the district, the physician-patient ratio ranges from 44.9 % (undersupply) to > 500 % (oversupply). Similar differences exist regarding accessibility. Although 94.5 % of the population of Hamburg is able to reach the nearest dermatologist within ten minutes (by car), it may take more than 30 minutes depending on district and mode of transportation. CONCLUSIONS: Analysis of the physician-patient ratio reveals differences regarding dermatological care in Hamburg. However, results of the network analysis show that these differences do not significantly affect access to dermatological care. Therefore, network analysis should be used as an additional tool to evaluate regional healthcare provision.

Downgrading recent estimates of land available for biofuel production.

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development.

Combining high biodiversity with high yields in tropical agroforests.

Local and landscape-scale agricultural intensification is a major driver of global biodiversity loss. Controversially discussed solutions include wildlife-friendly farming or combining high-intensity farming with land-sparing for nature. Here, we integrate biodiversity and crop productivity data for smallholder cacao in Indonesia to exemplify for tropical agroforests that there is little relationship between yield and biodiversity under current management, opening substantial opportunities for wildlife-friendly management. Species richness of trees, fungi, invertebrates, and vertebrates did not decrease with yield. Moderate shade, adequate labor, and input level can be combined with a complex habitat structure to provide high biodiversity as well as high yields. Although livelihood impacts are held up as a major obstacle for wildlife-friendly farming in the tropics, our results suggest that in some situations, agroforests can be designed to optimize both biodiversity and crop production benefits without adding pressure to convert natural habitat to farmland.

Multi-Source Remotely Sensed Data Combination: Projection Transformation Gap-Fill Procedure.

In this work a new gap-fill technique entitled projection transformation has been developed and used for filling missed parts of remotely sensed imagery. In general techniques for filling missed area of an image are broken down into three main categories: multi-source techniques that take the advantages of other data sources (e.g. using cloud free images to reconstruct the cloudy areas of other images); the second ones fabricate the gap areas using non-gapped parts of an image itself, this group of techniques are referred to as single-source gap-fill procedures; and third group contains methods that make up a combination of both mentioned techniques, therefore they are called hybrid gap-fill procedures. Here a new developed multi-source methodology called projection transformation for filling a simulated gapped area in the Landsat7/ETM+ imagery is introduced. The auxiliary imagery to filling the gaps is an earlier obtained L7/ETM+ imagery. Ability of the technique was evaluated from three points of view: statistical accuracy measuring, visual comparison, and post classification accuracy assessment. These evaluation indicators are compared to the results obtained from a commonly used technique by the USGS as Local Linear Histogram Matching (LLHM) [1]. Results show the superiority of our technique over LLHM in almost all aspects of accuracy.

Tradeoffs between income, biodiversity, and ecosystem functioning during tropical rainforest conversion and agroforestry intensification.

Losses of biodiversity and ecosystem functioning due to rainforest destruction and agricultural intensification are prime concerns for science and society alike. Potentially, ecosystems show nonlinear responses to land-use intensification that would open management options with limited ecological losses but satisfying economic gains. However, multidisciplinary studies to quantify ecological losses and socioeconomic tradeoffs under different management options are rare. Here, we evaluate opposing land use strategies in cacao agroforestry in Sulawesi, Indonesia, by using data on species richness of nine plant and animal taxa, six related ecosystem functions, and on socioeconomic drivers of agroforestry expansion. Expansion of cacao cultivation by 230% in the last two decades was triggered not only by economic market mechanisms, but also by rarely considered cultural factors. Transformation from near-primary forest to agroforestry had little effect on overall species richness, but reduced plant biomass and carbon storage by approximately 75% and species richness of forest-using species by approximately 60%. In contrast, increased land use intensity in cacao agroforestry, coupled with a reduction in shade tree cover from 80% to 40%, caused only minor quantitative changes in biodiversity and maintained high levels of ecosystem functioning while doubling farmers' net income. However, unshaded systems further increased income by approximately 40%, implying that current economic incentives and cultural preferences for new intensification practices put shaded systems at risk. We conclude that low-shade agroforestry provides the best available compromise between economic forces and ecological needs. Certification schemes for shade-grown crops may provide a market-based mechanism to slow down current intensification trends.