PioLaG: a piosphere landscape generator for savanna rangeland modelling

Heß, Bastian; Dreber, Niels; Liu, Yihui; Wiegand, Kerstin; Ludwig, Marvin; Meyer, Hanna; Meyer, Katrin

doi:10.1007/s10980-020-01066-w

Cited by 10 publications

(5 citation statements)

References 56 publications

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“…In many rangelands, the distance from watering points determines the grazing pressure on vegetation. Grazing gradients, or piospheres (Lange 1969) out from watering points, and the effects on plant species abundance can be modelled (Hess et al 2020). This approach used by Hess et al (2020) has value for modelling change (degradation or restoration) if critical thresholds are crossed.…”

Section: Critical Thresholds For Maintaining Populations Of Plantsmentioning

confidence: 99%

“…Grazing gradients, or piospheres (Lange 1969) out from watering points, and the effects on plant species abundance can be modelled (Hess et al 2020). This approach used by Hess et al (2020) has value for modelling change (degradation or restoration) if critical thresholds are crossed. Images from drones could be used for determining changes in plant communities and surface soil features out from water points that are beyond the resolution of satellite imagery.…”

Section: Critical Thresholds For Maintaining Populations Of Plantsmentioning

confidence: 99%

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Preventing rangeland degradation: a shared problem for Australia and China

Hodgkinson

Wang

2020

Rangel. J.

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Rangeland degradation continues in Australia, China and elsewhere. The stocking rate/animal production relationship has been a successful concept for pastoralists wanting to avoid degradation and/or raise incomes. However, there are no means available of alerting pastoralists to the approach of critical thresholds that would ‘flip’ rangelands into alternative states when grazing-stressed. Critical threshold forecasting for avoiding degradation (and seizing restoration opportunities) could be made available online. Research has yet to find, assemble and test the set of indicators needed to forecast the approach of critical thresholds envisaged in State-and-Transition thinking. Forecasting at paddock, property and regional scales would have to involve high-performance computing because the thresholds will be space and time dependent. The case for Australia and China to contribute cooperatively to this research effort rests on the large number of contrasting rangeland ecosystems across the two countries that represent rangelands globally. A proven history of past collaboration is extant with existing research programs on plant population dynamics, landscape patchiness/leakiness and soil biota status, and their responses to the separate and combined effects of climate and grazing animals. The road to adoption would involve partnerships with pastoralists throughout the process, remote sensing to identify approaching thresholds in real time, application of high-performance computing and possibly artificial intelligence, and packaging of forecasts for different socio-economic rangeland systems.

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Section: Critical Thresholds For Maintaining Populations Of Plantsmentioning

confidence: 99%

Section: Critical Thresholds For Maintaining Populations Of Plantsmentioning

confidence: 99%

Preventing rangeland degradation: a shared problem for Australia and China

Hodgkinson

Wang

2020

Rangel. J.

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“…The default test scenario uses a landscape created with the PioLaG Piosphere Landscape Generator (Hess et al 2020). It has a spatial extent of 400 x 400 cells i.e.…”

Section: Resultsmentioning

confidence: 99%

“…We collected also some anecdotal information from two farmers and a game reserve/wildlife park ranger from the mesic savannas and bush-encroached grasslands in the Louwsburg area (KwaZulu-Natal province) with a MAP of more than 700 mm. In addition, we organized and conducted a series of workshops, one with rangeland experts (July 2016, Kimberley, South Africa; 25 participants) and two with commercial farmers (February 2017, Ganyesa and Askham, South Africa; 29 and 28 participants, respectively) (Hess et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The landscape dimensions, the spatial distribution of vegetation types (as a layer), the farm position and number (as a layer) (it is possible to work with different farms in one simulation extent but for runtime and to simplify we only work with a single farm in this study), the management type(communal, commercial, reserve), camp number and layout (as a layer), number and position of watering points (as a layer), as well as soil properties and fire parameters are set up. As input, Midessa can either use classified aerial images (taken by satellites, airplanes or drones), and manually created or adapted maps, or the simpler approach is to use the Piosphere Landscape Generator PioLaG(Hess et al 2020). This landscape generator allows the user to easily create realistic savanna landscapes affected by livestock grazing/browsing.…”

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confidence: 99%

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Modelling, management and restoration of savannas in southern Africa

Heß¹

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Context -Bush encroachment has been observed in southern African savannas for several decades. This form of land degradation reduces the abundance of (palatable) grass biomass on farms, which in turn can support less livestock sustainably. A reduction in livestock usually results in lower income or less food for farmers and can therefore threaten livelihoods. The vegetation dynami cs of savannas are complex and multifactorially interconnected. Spatially explicit, process-oriented rangeland models are ideal tools to study these complex vegetation dynamics and the phenomenon of bush encroachment. These models can aid in optimizing management to reduce bush encroachment and give guidance to focus future research on the most relevant factors influencing vegetation processes and bush encroachment in savannas.Objectives -This dissertation aims to identify and examine the key factors that can cause and influence the emergence and expansion of bush encroachment in southern African savannas. To this end, we incorporated potentially important factors and processes in a simulation model to study their impact on bush encroachment and to be able to test different environmental and management scenarios with a focus on realistic vegetation dynamics and livestock management.Methods -To identify the potentially most important environmental and management factors that influence the vegetation dynamics of bush encroachment, we interviewed farmers and stakeholders, held workshops and had discussions with scientists and leading experts in the field.Through programming a landscape generator (PioLaG) and a process-oriented rangeland simulation model (Midessa), we gained the ability to set these influential factors and processes in relation to another and to observe and test their cause-and-effect relationship in terms of the impact on vegetation type distribution and livestock condition.Results -The most important factors that determine the current state of savanna rangelands are heterogeneous precipitation patterns, soil moisture, vegetation competition and succession, fire events, seed availability, grazing and browsing, wood reduction by firewood remo val or through arboricides, as well as other management factors such as livestock type and stocking density, type and characteristics of the grazing system, and farmer back-up plans for drought periods. Simulation results showed that introducing browsers and/or mixed feeders to a farm will greatly slow down bush encroachment. A grazer/browser mix thus seems to be a promising approach for a nonchemical management tool against bush encroachment. Careful and adaptive farming strategies including a sophisticated rotational grazing system, will improve vegetation conditions for livestock.Reducing the livestock density on a farm has also been found to slow down bush encroachment and delay the decline of livestock condition. From a modelling perspective , an interesting observation was that spatially heterogeneous distributed precipitation had a major impact on Abstract II simulation results ...

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Zoogeomorphology of Botswana

Perkins

2022

Landscapes and Landforms of Botswana

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PioLaG: a piosphere landscape generator for savanna rangeland modelling

Cited by 10 publications

References 56 publications

Preventing rangeland degradation: a shared problem for Australia and China

Preventing rangeland degradation: a shared problem for Australia and China

Modelling, management and restoration of savannas in southern Africa

Zoogeomorphology of Botswana

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