Big landscapes meet big data: Informing grazing management in a variable and changing world

Hudson, Tipton D.; Reeves, Matthew C.; Hall, Sonia A.; Yorgey, Georgine; Neibergs, J. Shannon

doi:10.1016/j.rala.2020.10.006

Cited by 12 publications

(8 citation statements)

References 47 publications

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“…Some of this herbaceous ANPP will be on inaccessible terrain, some less palatable, and some must be left ungrazed so that plants can regenerate. For mesic rangelands, GIs between 40 and 50% of total herbaceous ANPP are suggested for sustainability, while in more prevalent semiarid rangelands [69], maximum GIs between 25 and 35% are recommended [70]. By nature, mesic rangelands are more likely to be converted to croplands, leaving the remaining semi-arid to arid rangelands that are already approaching the upper GI limit recommended for sustainability.…”

Section: Global Gimentioning

confidence: 99%

Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010

2021

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Livestock grazing occupies ca. 25% of global ice-free land, removing large quantities of carbon (C) from global rangelands (here, including grass- and shrublands). The proportion of total livestock intake that is supplied by grazing (GP) is estimated at >50%, larger than the proportion from crop- and byproduct-derived fodders. Both rangeland productivity and its consumption through grazing are difficult to quantify, as is grazing intensity (GI), the proportion of annual aboveground net primary productivity (ANPP) removed from rangelands by grazing livestock. We develop national or sub-national level estimates of GI and GP for 2000–2010, using remote sensing products, inventory data, and model simulations, and accounting for recent changes in livestock intake, fodder losses and waste, and national cropland use intensities. Over the 11 study years, multi-model average global rangeland ANPP varied between the values of 13.0 Pg C in 2002 and 13.96 Pg C in 2000. The global requirement for grazing intake increased monotonically by 18%, from 1.54 in 2000 to 1.82 Pg C in 2010. Although total global rangeland ANPP is roughly an order of magnitude larger than grazing demand, much of this total ANPP is unavailable for grazing, and national or sub-national deficits between intake requirements and available rangeland ANPP occurred in each year, totaling 36.6 Tg C (2.4% of total grazing intake requirement) in 2000, and an unprecedented 77.8 Tg C (4.3% of global grazing intake requirement) in 2010. After accounting for these deficits, global average GI ranged from 10.7% in 2000 to 12.6% in 2009 and 2010. The annually increasing grazing deficits suggest that rangelands are under significant pressure to accommodate rising grazing demand. Greater focus on observing, understanding, and managing the role of rangelands in feeding livestock, providing ecosystem services, and as part of the global C cycle, is warranted.

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Section: Global Gimentioning

confidence: 99%

Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010

2021

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“…The Rangeland Analysis Platform (https://rangelands.app/ accessed on 5 January 2022) is a new tool utilizing satellite imagery and big data to help private and public land managers in the US to observe current and past rangeland conditions to aid in research and decision-making processes [19,20]. Potential applications of these datasets include: determination of appropriate stocking rates [21], identifying indicators of rangeland health, locating areas impacted by overgrazing, identifying state and transition phases [22][23][24], training new land managers on the history of management areas and allotments, and developing new management schemes by learning from past management practices [14,17,18,25].…”

Section: Satellite Imagery Applicationsmentioning

confidence: 99%

“…Advances in cloud storage and computing will be a major asset as geospatial data continues to grow in both resolution and file size. Geospatial and big data integration will be critical to the development of state-of-the-art decision support tools [24,121]. Development of crowd-sourced apps, such as LandPKS [22,23,122] and iNaturalist [123], provides citizen scientists a way to collaborate and provide geotagged field data to inform soil, water, grazing, and wildlife management research and objectives.…”

Section: Future Directionsmentioning

confidence: 99%

Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management

et al. 2022

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The use of geospatial sciences and technologies for the management of grazinglands has fostered a plethora of applications related to ecology, wildlife, vegetation science, forage productivity and quality, and animal husbandry. Some of the earliest use of remote sensing dates to the proliferation of aerial photography in the 1930s. Today, remote sensing using satellite imagery, global navigation satellite systems (GNSS), and internet-connected devices and sensors allow for real- and near real-time modeling and observation of grazingland resources. In this special issue of Remote Sensing, we introduce nine original publications focusing on varying aspects of grazingland management, such as animal health and telemetry, climate change, soil moisture, herbaceous biomass, and vegetation phenology. The work in this issue spans a diverse range of scale from satellite to unmanned aerial systems imagery, as well as ground-based measurements from mounted cameras, telemetry devices, and datalogging devices. Remote sensing-based technologies continue to evolve, allowing us to address critical issues facing grazingland management such as climate change, restoration, forage abundance and quality, and animal behavior, production, and welfare.

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“…Conventional methods for monitoring pasture biomass and livestock utilisation (i.e., ground-based measurement and proximal sensing) are limited in terms of scope, and both spatial and temporal extent [28]. Previous research in Australia [29], United Kingdom [30], New Zealand [31], and the United States [32] has reported limitations of ground sampling approaches (i.e., visual, rising plate meter and destructive method by clipping) in quantifying the spatial variability of pasture biomass.…”

Section: Introductionmentioning

confidence: 99%

“…The study showed that the accuracy of ANN improved when meteorological variables were included in the model; indeed, much process-based modelling is based primarily on longitudinal measurements of climate at a given site [2,34,40]. However, process-based applications are 3 of 30 required as an operational service to support farm management -what is often known as a decision support system (DSS) [16,28,41] -and is often limited by the accuracy of sitespecific soil characterisation [42,43].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Grassland Biomass and Regenerative Grazing Using Satellite Remote Sensing and Machine Learning

Ogungbuyi¹,

Guerschman²,

Fischer³

et al. 2023

Preprint

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The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely land management of sustainability indicators such as ground cover and grassland biomass, given the uncertainty of future climate and drought conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while maximising pasture biomass productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 hectare) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months) with each compared with control treatments with lighter stocking rates for longer periods (2,000 DSE). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica, and cocksfoot (Dactylis glomerata) were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We then invoked a machine learning model using Sentinel-2 imagery to quantify TSDM, standing green biomass and standing dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM and green biomass. However, regenerative treatments significantly impacted litter fall and trampled material, with the high intensity grazing treatments causing more dry matter trampling, increasing litter, enhancing decomposition rates and surface organic matter. Pasture digestibility was greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were significantly greater for the treatment with 15-month spell periods. Estimates of TSDM using machine learning with Sentinel-2 imagery underestimated TSDM in treatment plots but explained spatiotemporal variability associated within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3-6 months) are most conducive to increasing pasture production under high rainfall conditions, and we speculate that high intensity grazing is likely to positively impact on soil organic carbon through increased litterfall and trampling. Our study paves the way forward for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling management of pastures from afar.

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Big landscapes meet big data: Informing grazing management in a variable and changing world

Cited by 12 publications

References 47 publications

Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010

Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010

Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management

Quantifying Grassland Biomass and Regenerative Grazing Using Satellite Remote Sensing and Machine Learning

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