Remote sensing-based spatio-temporal modeling to predict biomass in Sahelian grazing ecosystem

Bénié, G.B.; Kaboré, Séraphine; Goı̈ta, Kalifa; Courel, Marie-Françoise

doi:10.1016/j.ecolmodel.2004.10.012

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…The capacity of models to predict the impacts of climate change on both yields and the nutritive value of forages needs to improve, in order to support policy choices and management decisions aimed at optimizing these parameters (Höglind and Bonesmo, 2002;Jégo et al, 2013;Jing et al, 2013). Lessons may be learnt from modeling developed for non-European semi-arid grazing lands, for example relating to the impact of grazing on erosion (Bénié et al, 2005). Integrated approaches including environmental and socio-economic aspects of grassland systems, such as the Sustainability and Organic Livestock Model (SOL) (FAO, 2012) demonstrate potential pathways for improving grassland modeling in the context of climate change.…”

Section: Modeling Grassland Productivity and Nutritional Valuementioning

confidence: 99%

Modeling European ruminant production systems: Facing the challenges of climate change

Kipling

Bannink

Bellocchi³

et al. 2016

Agricultural Systems

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This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.agsy.2016.05.007Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensification of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a priority. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are applied to develop ?smart? empirical modeling, may overcome the trade-off between complexity and usability. Developing the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems related to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks.authorsversionPeer reviewe

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Section: Modeling Grassland Productivity and Nutritional Valuementioning

confidence: 99%

Modeling European ruminant production systems: Facing the challenges of climate change

Kipling

Bannink

Bellocchi³

et al. 2016

Agricultural Systems

View full text Add to dashboard Cite

show abstract

“…This may prompt us to investigate the use of modelling approaches developed for non-European semi-arid grazing lands. For example, Benie et al [86] modelled the impact of grazing intensity on erosion risk in semi-arid grasslands in the Sahel. For grasslands in cold temperate regions, long-term predictions should also take into account the modifying effect of low temperature related stress on vegetation composition and productivity [52,70].…”

Section: Modelling For Long-term Predictionmentioning

confidence: 99%

“…Tall tower eddy covariance measurements with large spatial footprints and remote sensing allow coverage of large areas at increasing spatial resolution. These data are used to calibrate grassland models aimed at estimating greenhouse gas fluxes and biomass [85,86] but are generally not linked to any biodiversity research. Jing et al [87] demonstrated the importance of belowground biodiversity for ecosystem multifunctionality at 60 sites on the Tibetan Plateau, covering an area of over one million km 2 .…”

Section: Data and Inferences From Experimental And Observational Studiesmentioning

confidence: 99%

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

2018

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There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types.

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“…The Normalized Difference Vegetation Index (NDVI) is the most commonly used satellite index in the region for quantifying the temporal monitoring of vegetation [17]. Another widely used indicator is Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), recognized as a key variable in the assessment of vegetation productivity [13,18,19].…”

Section: Introductionmentioning

confidence: 99%

Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series

et al. 2015

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Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables-large seasonal integral (LINTG), length of growing season, and end of season decreasing rate-performed best (MAE = 605 kg· DM/ha; R 2 = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999-2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg· DM/ha; R 2 = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg· DM/ha; R 2 = 0.77),indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas

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Remote sensing-based spatio-temporal modeling to predict biomass in Sahelian grazing ecosystem

Cited by 20 publications

References 25 publications

Modeling European ruminant production systems: Facing the challenges of climate change

Modeling European ruminant production systems: Facing the challenges of climate change

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series

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