| 1. |
- Karlson, Martin, 1980-, et al.
(författare)
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Exploring the landscape scale influences of tree cover on crop yield in an agroforestry parkland using satellite data and spatial statistics
- 2023
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Ingår i: Journal of Arid Environments. - : Elsevier. - 0140-1963 .- 1095-922X. ; 218
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Tidskriftsartikel (refereegranskat)abstract
- Trees in agroforestry parklands influence crops both through competitive and facilitative mechanism, but the effects are challenging to disentangle due to the complexity of the system with high variability in tree cover structure and species diversity and crop combinations. Focusing on a landscape in central Burkina Faso domi- nated by Vitellaria paradoxa and Parkia biglobosa, this paper examines how tree cover influences crop yield at landscape scale using satellite data and spatial statistics. Our analysis is based on data from 2017 to 2018 with differences in rainfall to assess the stability in identified relationships. Our findings showed that tree canopy cover and tree density inside the fields tended to decrease crop yield because of competition, but also that these variables when considering the surrounding landscape exerted an opposite effect because of their buffering ef- fects. The explanatory variables representing soil properties did have limited effects on crop yield in this study. These patterns were consistent during the two years of monitoring. Overall, our results suggest that farmers in this area might manage the tree cover in a way that optimizes sustainable yields as canopy cover and tree density in most parklands is below the limits identified here where competition outweight the facilitative effects.
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| 2. |
- Karlson, Martin, 1980-, et al.
(författare)
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Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest
- 2015
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 7:8, s. 10017-10041
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and timely maps of tree cover attributes are important tools for environmental research and natural resource management. We evaluate the utility of Landsat 8 for mapping tree canopy cover (TCC) and aboveground biomass (AGB) in a woodland landscape in Burkina Faso. Field data and WorldView-2 imagery were used to assemble the reference dataset. Spectral, texture, and phenology predictor variables were extracted from Landsat 8 imagery and used as input to Random Forest (RF) models. RF models based on multi-temporal and single date imagery were compared to determine the influence of phenology predictor variables. The effect of reducing the number of predictor variables on the RF predictions was also investigated. The model error was assessed using 10-fold cross validation. The most accurate models were created using multi-temporal imagery and variable selection, for both TCC (five predictor variables) and AGB (four predictor variables). The coefficient of determination of predicted versus observed values was 0.77 for TCC (RMSE = 8.9%) and 0.57 for AGB (RMSE = 17.6 tons∙ha−1). This mapping approach is based on freely available Landsat 8 data and relatively simple analytical methods, and is therefore applicable in woodland areas where sufficient reference data are available.
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| 3. |
- Karlson, Martin, et al.
(författare)
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The Potential of Sentinel-2 for Crop Production Estimation in a Smallholder Agroforestry Landscape, Burkina Faso
- 2020
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media SA. - 2296-665X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Crop production statistics at the field scale are scarce in African countries, limiting potential research on yield gaps as well as monitoring related to food security. This paper examines the potential of using Sentinel-2 time series data to derive spatially explicit estimates of crop production in an agroforestry parkland in central Burkina Faso. This type of landscape is characterized by agricultural fields where cereals (millet and sorghum) and legumes (cowpea) are intercropped under a relatively dense tree canopy. We measured total above ground biomass (AGB) and grain yield in 22 field plots at the end of two growing seasons (2017 and 2018) that differed in rainfall timing and amount. Linear regression models were developed using the in situ crop production estimates and temporal metrics derived from Sentinel-2 time series. We studied several important aspects of satellite-based crop production estimation, including (i) choice of vegetation indices, (ii) effectiveness of different time periods for image acquisition and temporal metrics, (iii) consistency of the method between years, and (iv) influence of intercropping and trees on accuracy of the estimates. Our results show that Sentinel-2 data were able to explain between 41 and 80% of the variation in the in situ crop production measurements, with relative root mean square error for AGB estimates ranging between 31 and 63% in 2017 and 2018, respectively, depending on temporal metric used as estimator. Neither intercropping of cereals and legumes nor tree canopy cover appeared to influence the relationship between the satellite-derived estimators and crop production. However, inter-annual rainfall variations in 2017 and 2018 resulted in different ratios of AGB to grain yield, and additionally, the most effective temporal metric for estimating crop production differed between years. Overall, this study demonstrates that Sentinel-2 data can be an important resource for upscaling field measurements of crop production in this agroforestry system in Burkina Faso. The results may be applicable in other areas with similar agricultural systems and increase the availability of crop production statistics.
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| 4. |
- Sanou, Josias, et al.
(författare)
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Assessing Trade-Offs between Agricultural Productivity and Ecosystem Functions : A Review of Science-Based Tools?
- 2023
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Ingår i: Land. - : MDPI. - 2073-445X. ; 12:7
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Forskningsöversikt (refereegranskat)abstract
- Global population growth, especially in developing countries, will most likely require an increase in agricultural production, but the sustainability of this production cannot be achieved without the preservation of ecosystem functions. Therefore, farmers need to know about, and deal with, the trade-offs between agricultural productivity and ecosystem functions and services. This review aims to assess practical science-based tools that can be used to make decisions for sustainable agricultural production. We reviewed 184 articles and divided them into categories depending on whether they describe tools, practices, ecosystem services, models, or other topics. Although many studies were global in scope, the approach to analyzing and assessing trade-offs appears to vary geographically. The review showed that trade-offs between agricultural productivity and ecosystem functions are most commonly studied in Europe and Asia, while few studies have been conducted in sub-Saharan Africa. Most tools in the review addressed only one or a bundle of ecosystem services, related to water, biodiversity, or climate regulation, and were designed for different types of land use and ecosystems and applicable at different scales. More practical tools for trade-off analysis have mainly been developed and applied by development organizations with support from science. Closer collaboration between practitioners, development organizations, and scientists is suggested to foster co-development of tools useful for identifying sustainable strategies for closing the yield gap, increasing productivity and for balancing ecosystem services, building on the Sustainable Development Goal’s framework and its targets for agricultural productivity and ecosystem services for trade-off analysis. We recommend the development and fine-tuning of the identified tools to specific contexts and landscapes through innovation platforms bringing together farmers, extension workers, scientists, and local decision-makers.
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